November 2019 - ARC IWE CONSULTING

Practical guide for implementing IT service management frameworks and standards just out

Saving money, pleasing customers and getting the most out of technologies are just some of the benefits of an IT service management system (SMS). A new handbook provides guidance on how to get the best out of an SMS, in particular using ISO/IEC 20000.

ISO/IEC 20000, IT service management – Service management – A practical guide mainly relates to ISO/IEC 20000-1, Information technology – Service management – Part 1: Service management system requirements, the industry’s key International Standard for an IT SMS. It provides an easy-to-use overview of what is required to implement the standard effectively and proposes other standards and frameworks that can be used to fulfil the requirements.

Dr Suzanne Van Hove, who was involved in the development of the guide, said it is intended for those less familiar with the standard and who would benefit from clear information in non-technical language.

“Implementing an SMS in a structured way brings many benefits to an organization such as greater efficiencies and improved customer relations, but it should not be an added burden,” she said.

“This new handbook shows how ISO/IEC 20000 is relevant in today’s digital environment and how it fits nicely with popular SMS methodologies such as Lean, Agile and DevOps.”

ISO/IEC 20000, IT service management – Service management – A practical guide enables the implementation of practices that are beneficial and add value to an organization in a clear and proficient manner. It is descriptive and inclusive, as it takes into account the specific needs of an organization and allows them to adapt it how they see fit.

The handbook features three main sections: a practical guide to the implementation of ISO/IEC 20000-1; ways of measurably improving an organization’s service management processes including aspects such as process maturity, company culture and communication; and guidance on the use of other standards and frameworks in combination with the ISO/IEC 20000 series.

It was developed by ISO and the International Electrotechnical Commission (IEC) joint technical subcommittee ISO/JTC 1/SC 40, IT Service Management and IT Governance, whose secretariat is held by Standards Australia, ISO’s member for Australia. It is available for purchase from your national ISO member or through the ISO Store.

A practical guide on how to manage services in today’s dynamic service environment using ISO/IEC 20000-1:2018.

By Arc Consulting|2019-11-28T08:15:47+00:00November 28th, 2019|Weld Engineering Services|Comments Off

The Internet of Things has revolutionized our world by making everyday objects connected, intelligent and interactive. The Internet of Media Things allows media such as video and audio to join the party. A new series of ISO and IEC International Standards will enable the harmonized synchronization that is essential for this phenomenon to grow.

Internet of Media Things (IoMT) has the potential to change our world through massive-scale data exchange. But synchronization and interoperability are vital for this to work. ISO/IEC 23093, the series of International Standards for the Internet of Media Things developed by ISO and the International Electrotechnical Commission (IEC), provides the requirements and common language to enable media devices, applications and services to work together, outlining an architecture and specifications for the effective flow of data between media things.

The series provides a framework that can be used across technologies and national boundaries, enabling communication, storage, analysis, interpretation and retrieval of media big data emerging from large-scale IoMT devices. These standards therefore make it possible to realize large-scale interoperable IoMT applications.

The first two standards in the series have just been published and specify application programming interfaces (APIs) and the tools for use when it comes to the exchange of data between applications.

ISO/IEC 23093-2, Information technology – Internet of media things – Part 2: Discovery and communication API, specifies the APIs to discover media of things in the network, and communicate between them, along with APIs to facilitate transactions.

ISO/IEC 23093-3, Information technology – Internet of media things – Part 3: Media data formats and APIs, contains the tools to describe the data exchanged between media things, such as media sensors and analysers for their APIs.

A further two standards in the series, due to be published next year, will cover, respectively, architecture and reference software and conformance.

Teruhiko Suzuki, Chair of the ISO and IEC technical committee that developed the series of standards, said there are many areas where this technology can reduce costs and improve quality of life for people.

“In healthcare, for example, smart glasses that help the visually impaired to see better, or body sensors that help diabetics to better monitor their insulin levels, are just some of the many applications of this revolutionary technology,” he said.

Other examples of where the development of this technology can help improve the world include intelligent firefighting with Internet Protocol (IP) surveillance cameras and various aspects of smart manufacturing.

The ISO/IEC 23093 series of standards was developed by ISO/IEC JTC 1, Information technology, subcommittee SC 29, Coding of audio, picture, multimedia and hypermedia information, the secretariat of which is held by JISC, ISO’s member for Japan.

ISO/IEC 23093-2 and ISO/IEC 23093-3 are available from your national ISO member or through the ISO Store.

By Arc Consulting|2019-11-26T08:51:17+00:00November 26th, 2019|Weld Engineering Services|Comments Off

First International Standard for citywide events now in development

World-class events such as the Olympic Games can help put cities or regions on the map, all the while attracting valuable visitor revenue and economic investment. They do, however, bring many security risks. A new International Standard is currently being developed to help cities manage big events with public safety and security at their heart.

Elevated view of the New York Marathon crossing Verrazano Bridge in Staten Island, New York, USA.

ISO 22379, Security and resilience – Guidelines for hosting and organizing large citywide events, aims to provide guidelines and expertise on how to manage risks, public safety and service continuity during a wide-scale event. When published, it will be the first International Standard of its kind, bringing together the knowledge and know-how of experts involved in hosting major events such as the Tokyo Olympics 2020, the Winter Olympics in Beijing in 2022, the Berlin Marathon, and many more.

Ivar K. Lunde, Convenor of the working group that developed the standard, said that what makes it so valuable and unique is that it will be a product of the lessons and learnings of many cities and event organizers worldwide.

“Attracting international events is seen by many cities as a key way of promoting themselves on the global stage, but doing it successfully is a huge affair,” he said.

“What often happens currently is that each city starts its planning from scratch, without the benefit of the shared expertise and best practice of others. This standard will bring together such learning for everyone to benefit from.”

Lunde said there is as yet no “holistic” International Standard that can address all the key elements of preparing for, executing and evaluating a major event in a sustainable and secure manner. The use of ISO 22379 will therefore not only improve the success of large-scale events, but enable cities to host them in a way that also contributes to many of the United Nations Sustainable Development Goals.

It will also help cities decide whether to hold the event or not, as it enables them to identify the real risks and costs that will be involved.

ISO 22379 will be a useful complement to ISO 20121, Event sustainability management systems – Requirements with guidance for use, which provides a framework for making an event sustainable at the social, economic and environmental levels.

ISO 22379 is being developed by ISO technical committee ISO/TC 292, Security and resilience, the secretariat of which is held by SIS, ISO’s member in Sweden.

By Arc Consulting|2019-11-25T10:22:59+00:00November 25th, 2019|Weld Engineering Services|Comments Off

International Standards are essential tools for addressing many of the world’s pressing challenges, particularly those related to sustainable development. Recognizing the importance of this, ISO has a guide for all its standards developers on how to address sustainability issues in all ISO deliverables. It has just been updated.

ISO Guide 82, Guidelines for addressing sustainability in standards, provides advice to standards developers on how to take account of sustainability issues in the drafting or revision of ISO standards. It helps raise awareness of the challenges of sustainable development amongst standards writers and provides them with a systematic and consistent approach to identifying and assessing sustainability factors inherent in every standardization project. It also provides a way of reflecting those factors in the final text.

The guide has been updated to include information on how ISO standards can support the United Nations Sustainable Development Goals (UN SDGs), a global initiative designed to shift the world on to a more prosperous, inclusive and resilient path. It also provides guidance to identify partnerships with other organizations that would enhance the ability of integrating these SDGs in the drafting process.

Jimmy Yoler, Convenor of the expert working group that revised the guide, said it will ensure ISO standards continue to be relevant in helping governments, industry and consumers contribute to the achievement of the UN SDGs.

“This guidance will improve committee members’ understanding of what sustainability is, as well as its complexity. It will also encourage them to acquire extra expertise and partnerships in the field of sustainable development and to identify and address sustainability topics for standards development,” he said.

“This will bring added value to the contribution that ISO International Standards make to a better world for us all,” Yoler concludes.

The revised version of ISO Guide 82 is available from your national ISO member or through the ISO Store.

Organizations and companies looking to contribute to the SDGs will find that International Standards provide effective tools to help them rise to the challenge.

Developing sustainably

Find out how ISO Standards define responsible business and help advance the Global Agenda 2030.

By Arc Consulting|2019-11-20T08:17:58+00:00November 20th, 2019|Weld Engineering Services|Comments Off

More than four billion people in the world live without safely managed sanitation, impacting not only their health but their dignity. Recognizing the critical need for new and accessible technologies to remedy this situation, ISO has a number of International Standards to support innovative solutions and truly transform lives.

“Leaving no one behind” is the theme of this year’s World Toilet Day, an annual global event organized by UN Water on 19 November to raise awareness and inspire action to tackle the global sanitation crisis. It is also a key objective of the United Nations Sustainable Development Goals. This year’s theme aims to demonstrate that a toilet is not just a toilet, but can save lives and dignity and provide opportunities.

Universal sanitation is also the intention of a number of ISO standards, recently published or in development, which play a crucial role in enabling new sanitary solutions to flourish. These include revolutionary new technologies such as stand-alone sanitation systems that safely treat waste without the need to be connected to a traditional sewerage system. They provide the solution for safe and hygienic toilets where they are needed most.

ISO 30500, Non-sewered sanitation systems – Prefabricated integrated treatment units – General safety and performance requirements for design and testing, supports the development and growth of this technology. Use of the standard helps to demonstrate to manufacturers, governments, regulators and end users of non-sewered facilities that they are safe, reliable and of good quality, thus encouraging further investment in the development of even better toilets.

Another solution for clean sanitation in places that lack traditional water utilities and sewerage systems is the use of on-site domestic wastewater treatment systems. Installed and managed correctly, they can be a hygienic, low-cost way of disposing of wastewater. However, many local communities lack the necessary knowledge and resources to set this up.

ISO 24521, Activities relating to drinking water and wastewater services – Guidelines for the management of basic on-site domestic wastewater services, offers the practical guidance required for designing and building such facilities as well as training up the people who are destined to use them.

Work is also underway on a standard for prefabricated systems that can not only treat human waste, but turn it into useful resources such as clean drinking water. ISO 31800, Faecal sludge treatment units – Energy independent, prefabricated, community-scale, resource recovery units – Safety and performance requirements, specifies requirements and test methods to ensure the performance and safety of units that can serve up to a hundred thousand people. Developed by an ISO expert committee in partnership with the Bill & Melinda Gates Foundation, it is due to be published sometime next year.

These are just some examples of where international expertise has come together to develop best-practice guidelines supporting solutions to the toilet problem. They also contribute directly to the United Nations Sustainable Development Goal 6 for clean water and sanitation, ensuring everyone has access to basic hygiene facilities by 2030.

Find out more about World Toilet Day on UN Water’s dedicated Website.

For more information about ISO standards for safe sanitation, contact your national ISO member or visit the ISO Store.

Ensure availability and sustainable management of water and sanitation for all

By Arc Consulting|2019-11-18T09:22:47+00:00November 18th, 2019|Weld Engineering Services|Comments Off

Assessing the Role of Big Data and the Internet of Things on the Transition to Circular Economy: Part II

For this study, a complete set of scientific publications was analysed. Regional and temporal characteristics are presented in Figure 1 (from first publication to 2018; total of 226 documents, including articles, reviews, conference papers and proceedings, filtered according to remarks presented in the Methodology section of Part I (1)) and Table I. Europe and Asia lead the interest in the subject mostly due to the efforts and regulations established by the EU and China governments. North America (here including Mexico and other Central American countries), despite the high level of development of the geographies, occupies only the third place in publications, with less than 15% of participation. This number also draws attention to the fact the USA is one of the major environmental polluter countries according to the United States Environmental Protection Agency (US EPA) (2), which reveals a context of significant research opportunities for the region.

Fig. 1.

Publication profile on CE and big data or IoT by region, total of 226 documents

Table I

Detailed Publication Profile on CE and Big Data or IoT by Region, Total of 226 Documents

Region	All Years	2007	2010	2012	2013	2014	2015	2016	2017	2018
Africa	3	0	0	0	0	0	0	1	0	2
Asia	77	0	2	0	4	4	8	7	19	33
Europe	103	1	0	2	5	4	9	14	28	40
North America	33	0	0	0	0	1	4	7	13	8
Oceania	6	0	0	0	0	0	0	2	1	3
South America	4	0	0	0	0	0	0	2	2	0
TOTAL	226	1	2	2	9	9	21	33	63	86

Considering all the publications, 53% came from scientific journals and 15 sources presented at least two publications on the subject. The Journal of Cleaner Production (ISSN 0959-6526) and Sustainability (ISSN 2071-1050) led with 19 and nine publications respectively, as shown in Appendix 1 (for all Appendices, see the Supplementary Information included with the online version of Part I (1)). The high number of other source documents (47%), along with the publication concentration in the past three years, may indicate science and academia are still in the early stages of development for the studied subjects.

The research also grouped publications according to the Standard Industrial Classification (SIC) codes (3). The majority of documents apply to public administration (32.3%), mostly because of smart city initiatives and suggests governments are leading initiatives and sponsoring research. A considerable number of publications (30.1%) were not allocated to a specific SIC code as they could not be related to any specific industry. Results are presented in Table II.

Table II

Publications by Industry Type with SIC Codes

Industry	SIC Codes	Number of publications	%
Public Administration	91–99	73	32.3%
Cross industry	n/a	68	30.1%
Manufacturing	20–39	18	8.0%
Construction	15–17	14	6.2%
Agriculture, Forestry, Fishing	01–09	11	4.9%
Transportation Equipment	37	8	3.5%
Business Services	73	7	3.1%
Private Households	88	5	2.2%
Engineering Services	8711	4	1.8%
Retail Trade	52–59	4	1.8%
Electric, Gas and Sanitary Services	49	3	1.3%
Transportation & Public Utilities	40–49	3	1.3%
Educational Services	82	2	0.9%
Mining	10–14	2	0.9%
Chemicals and Allied Products	28	1	0.4%
Computer and Office Equipment	357	1	0.4%
Food and Kindred Products	20	1	0.4%
Health Services	80	1	0.4%
TOTAL	–	226	99.9%

Documents were also grouped by methodology type, which demonstrates more interest in model development and reviews as shown in Figure 2. This indicates researchers have been putting more effort into standards, definitions, framework creation and reviews (which can be justified by the early stage of stability and maturity of the subjects). Other analysis was made according to CE principles (4) as demonstrated in Figure 3. The highest level of participation on the reduction principle suggests a major focus on changing consumer behaviour with the use of new technologies rather than investing in clean energy sources or extending product lifespans. On the other hand, the reclassification principle, despite its importance, still lacks technology efforts.

Fig. 2.

Methodologies applied on 226 mapped documents

Fig. 3.

CE principles identified in 226 mapped documents, some articles with more than one principle

Supplementary details regarding mapped documents, such as top publishing institutions, journals and authors are available in Appendix 1.

In Appendix 8 we also present some practical case studies mapped during the literature review for distinct industries and countries in order to illustrate how CE can be fostered by big data and internet of things (IoT).

1.1 Content Analysis

Research extracted the 150 most frequent words from the 226-article text corpus in order to verify and confirm that the resulting capabilities list is addressing the most relevant topics. The word cloud generated is shown in Figure 4.

Fig. 4.

Word cloud containing the 150 most frequent words from all 226 mapped articles

Bigram, trigram and four-gram generation proved to be a valuable insight resource as some compound expressions not only appeared in the top 150 list, but also performed as an important validation tool for capabilities generation (for example ‘cloud computing’, ‘energy consumption’ and ‘smart sustainable city’), all key aspects of the validated capabilities list.

The top 20 expressions mapped are presented in Table III. The complete list of top 150 expressions is available in Appendix 4.

Table III

Most Frequent 20 Expressions and Frequencies

Expression	Frequency	Expression	Frequency	Expression	Frequency
product	5658	design	3352	challenge	1470
energy	4894	system	3287	source	1466
process	4278	power	3187	technical	1463
develop	4241	city	3036	monitor	1450
service	3745	urban	2984	measure	1413
environment	3601	operation	2811	strategy	1395
time	3575	local	1481	–	–

The words ‘product’, ‘service’, ‘urban’ and ‘city’ all appeared with high frequency, indicating initiatives for different industry types can benefit from big data and IoT, for example, and therefore influenced the framework development (i.e. specific treatment for industry type). The same analysis was made for each expression, performing essentially as a verification tool to ensure the framework and capabilities were consistent.

1.2 Experts Review

The first version of the resulting capabilities framework was submitted to a group of domain experts who provided useful insights into the study. Table IV shows the main contributions accepted from the domain experts. Typographic errors, rephrasing, use of synonyms and other small revisions are not listed.

The list of domain experts is presented in Appendix 2.

Table IV

Domain Experts’ Main Contributions

CE principle	Contribution	Contributing expert^a
Design	Clarification on urban areas relation to public administration only	3, 4
Design	Added ISO 20400 – sustainable procurement (applies to reduction, reuse and recycle principles as well)	1
Reduction	Process postponing: inclusion of ‘no effectiveness loss’ condition	5
	Decentralised offices: only if proven to provide more efficient use of available resources	4, 5
	Added emissions monitoring	4
Reuse	Added marketplaces for sourcing, value and managing reusable materials	1
Recycle	Added disassembling and remanufacturing	4, 6
	Policies application rather than only having the policies documented	1, 4
	Use of electronic tags	1
	Added recyclable resin	1
Renewable energy	Net metering added to list	4
Renewable energy	Blockchain transactions added to list	2, 4

1.3 CE IT Capabilities Framework

The final framework resulted in a set of 39 capabilities divided according to the six CE principles and presented in Figure 5 and Table V. It builds on both the ReSOLVE framework (5) and the six CE principles (4). The mapped capabilities were separated into application groups and industries, as some are considered technological tools, others new processes, some long-term projects and others punctual actions.

Fig. 5.

The CE IT capabilities framework

Table V

Mapped Big Data or IoT Capabilities on CE Principles According to Literature Review

CE principle	Big data or IoT capabilities	Sample sources
Design (DS)	Parts made with compatible components with the support of modern technology based on artificial intelligence (AI), machine learning, big data or IoT that can be mixed after use without contamination for efficient recycling or upcycling or remanufacturing and designed for new uses, enhancing its after-use value Use of big data or analytics during product design or conception to provide sustainable feedstock and optimised resource use to reduce waste generation during manufacturing processes Product lifecycle management (PLM)^a concepts supported by big data or IoT to improve product design, such as modular or replaceable components Design and use of IT infrastructure for reuse or easy recyclability Use of sustainable design criteria on technology selection processes, such as design for recycle For public administration sector only: CE-planned urban areas designed and conceived according to smart city principles to optimise waste collection and value recovery with the use of IoT	(6–17)
Reduction (RD)	Minimise greenhouse gas and other pollutant emissions with the support of modern technologies such as analytics for monitoring and decision making Optimise materials savings through smart connected devices Use of decentralised IT technologies to provide resource use and consumption (either energy or components) reduction, such as cloud computing with big data, avoiding the need of robust local physical infrastructure PLM concepts supported by big data or IoT to reduce waste generation and disposal Use of smart sensors to monitor energy, water and other resource consumption in manufacturing processes Use of smart sensors to monitor energy, water and other resource consumption within facilities of organisations Machine behaviour monitoring to autonomously optimise energy, water and other resource consumption, even by postponing processes if necessary, without prejudice to process effectiveness Use of IT devices and infrastructure in a way that offers minimal environmental impact (green IT) by optimising energy consumption Use of technology-enabled decentralised offices and data centres proven to provide more efficient use of available resources (including human, for example no need to commute) Use of energy savings or minimum waste generation criteria on technology selection processes Energy efficiency improvement in data centres	(7, 12, 13, 16–28)
Reuse (RU)	Improve asset usage rates by applying CE business models such as leasing and ‘platform as a service’ (PaaS), enabled by IoT and big data Product lifetime extension by using connected devices to facilitate predictive maintenance PLM concepts supported by big data or IoT to improve product and component reusability Product to service (possession vs. use) transition enabled or leveraged by IT to improve usability rates Use of cloud-based marketplaces for sourcing, value and managing reusable materials IoT-enabled waste collection or reverse logistics for materials (such as packaging) reuse Monitor component location and quality in order to assess state and allow reuse Use of IT devices or infrastructure in a way that offers minimal environmental impact (green IT) by reusing components to their maximum Use of IoT devices to increase component sharing and reuse rates (such as in industrial symbiosis) Policies for extending IT infrastructure lifecycle (for example, donation) Use of product or component lifetime criteria on technology selection processes	(7, 9, 12, 13, 15–17, 25, 29–32)
Recycle (RY)	Apply AI to support ‘closing the loop’ on products and materials, allowing optimised product sorting and disassembly, remanufacturing and recycling PLM concepts supported by big data or IoT to improve product recyclability Use of IoT technologies to optimise waste collection and reverse logistics for recycling or upcycling, including the use of electronic tags on trash bins Use of IT devices or infrastructure in a way that offers minimal environmental impact (green IT) by applying recycling policies Use of IT infrastructure recycled from electronic waste Applied policies for discarding obsolete IT infrastructure in a sustainable (for recycle) manner Use of product recyclability (or made from recyclable resin) criteria on technology selection processes	(7, 9, 12, 13, 15–17, 25, 33)
Reclassification (RC)	Applying IoT integrated with AI to allow mixed industrial technical (non-organic) waste automated separation	(7, 34–36)
Renewable energy (RN)	Use of renewable energy sources (including light, motion, temperature) for IT devices to operate autonomously, mainly in poorly accessible remote areas Power IT devices or infrastructure with renewable clean energy Net metering-based^b renewable energy generation, monitoring, consumption and selling (leveraged by blockchain when applicable)	(25, 37–43)

The mapping considering each capability and the corresponding block of the framework is presented in Figure 6. Capabilities not related to any industry are considered as applicable to any (cross industry).

Fig. 6.

The CE IT capabilities framework with mapped capabilities

1.3.1 Framework Highlights

The ReSOLVE Framework itself promotes a direct application of modern technologies on the elements ‘optimise’ (leverage big data and automation), ‘virtualise’ (dematerialisation) and ‘exchange’ (for example three-dimensional printing). With the establishment of the CE IT capabilities framework, not only can new applications be observed to those elements, but also it is now possible to notice that all elements of ReSOLVE can benefit from cutting-edge technologies. For example: the ‘regenerate’ element can be leveraged with net metering and the use of solar energy allows the use of IoT based devices in remote areas, like agricultural crops; the ‘share’ element benefits from smart connected devices monitoring equipment’s usage and providing predictive maintenance data and technology also connects users with similar interests allowing higher usage levels; in ‘optimise’, waste reduction can take many advantages from technology, varying from the use of AI and machine learning on product design to optimise resource consumption to application of green IT to increase product efficiency; ‘loop’ benefits from the use of AI to allow closing the loop on materials and to optimise waste collection and reverse logistics with IoT; ‘virtualise’ links directly with cloud computing and the home office; and ‘exchange’ may use technology on product design to promote shifting to renewable materials feedstock.

2. Conclusions

The scientific interest in applying modern technologies such as big data or IoT in the transition to CE is growing. Articles from 2017 and 2018 alone account for 66% of all the publications on the subject to date, reflecting what takes place in practice, given the number of cases and models identified – 60% of all articles mapped. Nevertheless, from the 21 different CE frameworks identified, only three mention IT as a component, and most of them refer to EMF as a primary CE reference, some built on EMF’s ReSOLVE framework. Therefore, IT scientists, scholars and practitioners still do not have at their disposal a framework to be followed that would allow a technological gaps assessment. This framework development was the article’s main purpose, which identified 39 IT capabilities necessary for organisations to consider themselves technologically circular.

The main scientific contribution of this study was the extension of the existing ReSOLVE framework to a level of detail that will allow IT professionals to assess their current CE gaps and plan their actions to enable an easier transition to CE. Additionally, the role modern technologies aligned with Industry 4.0 play in the organisational transition to CE was identified, and the status quo of related research around the world and the most interested institutions and publications were described.

In addition to the traditional literature review of 226 articles retrieved from Scopus^® and Web of Science^TM databases, the following triangulations were carried out to allow research confirmation and comprehensiveness: content analysis through statistical tool ‘R’, grey literature analysis and expert opinions. The capabilities were then divided according to the six CE principles presented in the literature: six for the design principle, 11 for reduction, 11 for reuse, seven for recycling, one for reclassification and three for renewable energies. The findings indicate that there are principles currently more susceptible to IT than others and that the public administration sector has attracted more research interest in the area possibly because of current initiatives fostered by government entities and agencies.

The following future research opportunities originate directly from this study: the conception of a scale with metrics to allow organisations to self-assess and benchmark (i.e. how many and which capabilities should an organisation implement and to what extent before it can be considered circular); and the confirmation of the framework’s performance by applying it in the form of a questionnaire or survey against selected organisations of different ports and industries.

The limitations of the study lie mainly in the volatility of recent modern technologies that may not have a long lifecycle, making the framework obsolete in the short term. In addition, since it is an essentially theoretical study based on published documentation, it still lacks practical confirmation through organisational case studies.

By Arc Consulting|2019-11-15T14:28:31+00:00November 15th, 2019|Weld Engineering Services|Comments Off

How does an organization value the dependencies it has on the environment? There’s a lot of information on what makes smarter sustainable strategies, but very little data. Now a new ISO standard provides the answers.

There is certainly no shortage of cost/benefit analyses that organizations can use to value environmental aspects and their impacts. But which ones should organizations use? The choice is immense and often confusing. To change that, ISO has just published ISO 14007, Environmental management – Guidelines for determining environmental costs and benefits.

Organizations need to know which environmental measures and strategies are economically sound. For example, valuing natural resources and performing environmental cost-benefit analyses are both strategically and tactically important steps in sustainable development programmes.

ISO 14007 will enable organizations to determine and communicate the costs and benefits associated with their environmental aspects, impacts and dependencies on natural resources. It tells organizations how to carry out cost-benefit analyses for different environmental options.

Martin Baxter, Chair of ISO/TC 207’s subcommittee SC 1, Environmental management systems, explains. “There is a growing drive towards valuing natural capital, as well as a need to undertake a monetary assessment of an organization’s environmental aspects and impacts,” he says. “ISO 14007 helps in creating transparent and accurate data, which in turn may remove a hurdle for sustainable development – to understand the value of sustainability.”

The new standard complements ISO 14008, Monetary valuation of environmental impacts and related environmental aspects, published in March 2019. It describes methods for valuing environmental aspects and impacts, providing the essential data that feeds into such cost-benefit analyses. Hence, the two standards dovetail with one another.

“Sustainability is about preserving human well-being for present and future generations. And well-being is closely linked to an economy where natural capital plays a critical role for the future,” concludes Baxter.

ISO 14007 was developed by ISO technical committee ISO/TC 207, Environmental management, subcommittee SC 1, Environmental management systems, whose secretariat is held by SCC, ISO’s member for Canada. ISO 14007 is available from your national ISO member or through the ISO Store.

By Arc Consulting|2019-11-14T10:59:18+00:00November 14th, 2019|Weld Engineering Services|Comments Off

Assessing the Role of Big Data and the Internet of Things on the Transition to Circular Economy: Part I

IT plays an important role in enabling disruptive organisational transformations, despite the known privacy and confidentiality issues and security risks (1, 2) constantly being run up against with the use of technology itself (3). Recent studies show decision-making processes have become much faster and more precise in companies adopting big data technologies (4, 5) for example. Internal and external communications and knowledge sharing (6, 7), not only in large organisations but also in small and medium-sized enterprises (SMEs) (8), are faster and better due to social networks (9) and instant messaging technologies (10), just to mention a few recent examples. In the corporate sustainability (CS) and other environmental fields it is no different. Many recent studies focus on understanding the role of IT in offering solutions to reduce the negative impacts of organisations and society on the environment (11–13), including those generated by modern technologies, known as ‘green IT’ (14–17). Several other studies can be found in the literature. Large data vulnerability risks are also present in this context (18). One special concept based on the planet’s sustainability issues is gaining attention from organisations and government recently, namely CE.

Although the concept of CE has existed for decades, it has become more evident in the past few years as resources are becoming scarcer and more expensive, mainly because world population and resource consumption continue to grow for a limited-resource earth. Moreover, society is now more concerned about issues such as global warming, plastics and other waste disposal (19, 20) and aware of the need for stewardship of our planet’s natural resources (21). Another relevant factor is the quick development of automation technologies brought by what has been called the Fourth Industrial Revolution, also known as Industry 4.0, essentially leveraged by big data and the IoT, which are making the implementation of CE concepts not only possible and more economically feasible, but necessary (22, 23).

The role big data and IoT perform in enabling the transition to CE has been subject of many studies and is attracting the interest of the scientific community.

As organisations and governments are being pushed to take action to transform business and city models to enable CE, more efforts need to be taken in technology by IT professionals to make it a consistent, fast time-to-market and low-cost transition (24–28). However, the IT path to be followed by organisations and governments still lacks a structured framework, with some practitioners even questioning whether technologies such as big data really foster sustainability (29).

Researchers have been putting a lot of effort into establishing CE theories and models to provide useful and usable tools to help scientists and practitioners develop their work. Nevertheless, as such initiatives are usually undertaken independently and motivated by different interests, dozens of separate studies have arisen in recent years. Although CE has been known since the 1970s, more than 50% of all studies are published since 2014, as shown in Figure 1. Recent studies show more than 100 CE definitions have already been documented and published (30), along with dozens of frameworks, each one approaching CE from a different perspective. Although all offer significant contributions to science and practice, choosing one to perform as a baseline for research and business strategies development is challenging. When the IT component is added, the situation becomes even more complex, as new disruptive technologies arise very fast and accelerate the obsolescence of previous studies. Moreover, it is being noticed that current CE frameworks rarely explore the IT component (or ignore it, as described in Section 2.2), giving modern and disruptive technologies a secondary role on the transition to CE. An exception is the EMF ReSOLVE framework (31). It not only acts as a basis for other published frameworks, but also recognises the greater role IT performs in the transition to CE, yet it still lacks theoretical deepening, which is a gap to be addressed by this research.

Fig. 1.

Publication profile on CE for the years 1999–2018 (Source: Scopus^®)

This study intends not only to reinforce the key role technology performs on the transition to CE – specially big data and IoT, both the foundation of the so-called Industry 4.0, as already presented in some novel published studies (32, 33), but also proposes a preliminary framework for IT capabilities, built on EMF’s ReSOLVE framework, to be used by IT professionals in order to understand and assess their organisation’s gaps for the transition to CE. The framework was conceived based on a literature review composed of four separate sources: a traditional review of 226 big data or IoT applications on CE scientific articles, all retrieved from Scopus^® and Web of Science^TM databases; content analysis (simple bibliometric review) of the retrieved articles; industry, corporate and government initiatives (grey literature); and industry experts review. This triangulation was a necessary step not only for validity and reliability issues, but also because of the known gap between the academy and industry and private sector initiatives for the research subject (19), so each source provided complementary data. Therefore, this study aims to answer the following research question: What are the big data and IoT capabilities that IT professionals need to address in order to support their organisations in the transition to the CE?

The remainder of the paper is organised into four sections, starting with the literature review, including an analysis of the current CE available frameworks, followed by the methodologies applied to the research and a results and discussion section including the proposed IT capabilities framework. The final section (in Part II, (34)) presents the study’s conclusions, its limitations and future research recommendations.

2.1 Defining Circular Economy

For the past few years, the current production and consumption model essentially based on a linear flow (take-make-dispose), which generates an increasing throughput of natural resources, has brought back a concept originating during the 1970s and closely related to environmental concerns: CE. It is based on a circular system where both organic and technical wastes are minimised and returned as feedstock, leading to a zero waste generation model (20, 35–37), being restorative and regenerative by design and resting on the following principles: preserving and enhancing natural capital, optimising resource yields and fostering system effectiveness (38). The efficient use of energy (and its transition from fossil to clean and renewable sources) and the promotion of product reuse and lifetime extension actions also contribute to CE.

Currently only 9.1% of the world economy can be considered circular (39), meaning that around 90% of everything that is produced and consumed on the planet still follows the take-make-dispose flow. Furthermore, the world population continues to grow for a limited-resource earth (19, 20) with scarce commodities becoming more expensive (40). For instance, if we look at Brazil alone, only 1% of all organic waste is treated and beneficiated in a country where 50% of all wastes are organic, producing every year the amount of greenhouse gases equivalent to seven million cars (41).

Recently the United Nations Environment Programme (UNEP) developed a study focused on the issue of disposal of plastics in the ocean that led to the ‘Marine Plastic Debris and Microplastics: Global lessons and research to inspire action and guide policy change’ report. (42). Some estimates from the report indicate that the ‘visible’ part of marine debris (what is floating on the sea’s surface) represents only 15% of all marine debris while those on water columns account for another 15% and 70% of all marine debris is simply resting on the seabed. Moreover, most of this plastic breaks up into microplastic over time, representing a hazard to wildlife, fish and people.

These and many other documented facts demand action from governments, organisations and society, and CE is performing a critical role in this necessary transformation. For example, in 2018 the European Commission – the executive branch of the European Union – launched the 2018 Circular Economy Package, which is a set of measures aiming to transform Europe into a more sustainable continent (43), including challenging goals such as making all plastic packaging recyclable by 2030. It consists of several documents focused in legislations about plastics, waste and chemicals and proper communication to citizens. This was an outcome of the EU Circular Economy Action Plan established two years before. China is also making considerable progress transitioning to CE, being one of the first countries to have laws for CE development (after Germany and Japan) since 2009 and further detailed in 2013 with its Circular Economy Development Strategies and Action document, establishing directives for companies, industrial parks and even cities and regions (44). In addition, not only can CE address social and environmental issues, but it can also help develop the economy. In Europe alone, benefits of around €1.8 trillion may be achieved by 2030 (45).

Those and other initiatives led a number of organisations, including public administration and academia, to put more effort into developing or applying solutions and research to promote the transition from the linear to the circular model economy. Science has recently made significant progress in CE research. This can be seen by observing the academic research evolution presented in Figure 1, which shows that most CE research is relatively new (26% in 2017 and 2018 alone).

Nevertheless, as a consequence, a number of different theories and principles or constructs are still emerging, making the process of defining CE formally difficult. Therefore, one of the challenges faced by scholars is agreeing on a common theoretical scientific definition of CE and its various ramifications, as most of the successful initiatives have been almost exclusively led by practitioners.

Discussions about the concept’s incipience have already been the object of recent studies. For example, recent research put together 114 different CE definitions and coded them on 17 dimensions (30). Other studies propose taxonomies based on different methods (19, 46), while others try to establish a consensus for the adequate use of the term CE (47) and compare it to the concept of sustainable development (36). There is also some justified criticism regarding the correct use of the term CE (48) and even questions whether CE captures the environmental value propositions (49).

For this research, we identified a straightforward classification based on a comprehensive literature review article covering 20 years of CE studies, that groups it into six basic principles (20): (a) design, (b) reduction, (c) reuse, (d) recycle, (e) materials reclassification into technical or nutrients and (f) renewable energy. The study had more than 300 citations in three years since its publication in the Journal of Cleaner Production (ISSN 0959-6526), which is a very high impact factor journal. Details on each principle obtained from the literature can be found in the original publication.

2.2 Circular Economy Frameworks

There are several CE frameworks available in the literature. A query in Scopus^® with the expressions ‘circular economy’ and ‘framework’ in title, keyword and abstract retrieved 21 different models, the oldest published in 2016. Of those, we compared the nine most relevant ones (i.e. from journals with scimago >20 and with at least five citations), which are presented in detail in Appendix 9 (for all Appendices: see the Supplementary Information included with the online version of this article). Here we describe the top three: the most popular, ‘A comprehensive CE framework’ (50) was proposed through an extensive literature review and is based on economic benefits, environmental impact and resource scarcity and is focused in the manufacturing industry. The second is called ‘The 9R Framework’ (30). It extends the classical concept of 3Rs to nine definitions and suggests an increase of circularity for each one: Recovery of Energy (less circular: incineration), Recycle, Repurpose, Remanufacture, Refurbish, Repair, Reuse, Reduce, Rethink and Refuse (more circular: make product redundant). The third, ‘Circular economy product and business model strategy framework’ (51), proposes the need for design and business model strategies to be implemented in conjunction in order to better drive circularity. Although all are unique and proved to be valuable given their popularity, along with authors and publications relevance, two common characteristics were observed: they do not consider the ‘technology’ aspect; and all reference directly, as a main source of information, the EMF, known to lead and foster both theoretical and practical initiatives regarding CE since 2010. EMF created a framework called ReSOLVE, which is used as a basis by some of the top frameworks mapped (for example, the backcasting and eco-design for the circular economy (BECE) framework (52)) and is the most popular in internet search (see Appendix 9). It is considered part of the grey literature rather than a scientific document. It offers organisations a tool for generating circular strategies and growth initiatives, composed of the levers: (a) regenerate, (b) share, (c) optimise, (d) loop, (e) virtualise and (f) exchange. Moreover, technology is key: transformation of products into services, leveraging big data and automation and incentives to adopt new technologies (for example, three-dimensional printing) are all aspects considered by the ReSOLVE framework. Therefore, rather than proposing a new framework in this study, the authors decided to build the model on ReSOLVE.

2.3 Big Data and Internet of Things

The big data concept represents the ability to gather, process and analyse massive amounts of structured and non-structured data continuously (53, 54), transforming it into useful information for decision-making activities. Researchers have reduced the definition into the basic 4Vs (55, 56): (a) volume, (b) variety, (c) velocity and (d) veracity, representing its main characteristics. Other scholars have improved the definition and extended it with: (e) value (57, 58), (f) validity, (g) visualisation, (h) vulnerability, (i) volatility and (j) variability (59). Big data has already proved its importance for organisations, as for example in the health industry (60), general management (61) and government (24).

IoT is an emerging technology that enables data acquisition, transmission and exchange among electronic devices and targets enabling integration with every object through embedded systems (62). It has three main components: asset digitisation, asset data gathering and computational algorithms to control the system formed by the interconnected assets (63). One relevant data source may be considered for big data. Not only can it support applications such as providing better disease diagnostics and prevention, monitor stocks in real time (64) or aid the transportation of goods, but it also applies to basically any activity involving data monitoring and control, and information sharing and collaboration (65). This emerging term is considered key to enable technological solutions and is receiving industry-specific extensions such as in mining (metallurgical internet of things (m-IoT)) (66), industry (industrial internet of things (IIoT)) (67) and for environmental causes (environmental internet of things (EIoT)) (24, 68).

There are other concepts related to CE being leveraged by big data or IoT. They are described in Table I. In the context of CE for this research, servitisation relates to the reuse principle. It improves asset usage rates to their highest utility and value as the product ownership remains with the manufacturer, who is responsible not only for the proper product collection and disposal, but also for extending its lifetime and recapturing value through refurbishment and reuse. Sharing economy also explores the reuse and reduce principles as product owners can collaborate with each other in order to maximise the use of their own assets during their idle periods. For example, studies show cars stand idle for about 95% of the time (69). Smart cities relates essentially to the design principle as it consists basically in planning and reorganising urban areas.

Table I

Circular Economy-Related Concepts Leveraged by Big Data or IoT According to the Literature Review

Concept	Description	References
Servitisation	Shift from selling products to providing services with an emphasis on use rather than possession. Providers such as Netflix and Salesforce.com are examples of businesses born using the concept. Traditional companies such as Philips (selling lighting services instead of bulbs), Michelin (pay-by-the-kilometre services instead of tyres) and Renault (leasing batteries for electric cars) are shifting some of their business models to servitisation	(19, 70–77)
Sharing economy	Underused products, services or assets made available to third parties, paid or not. Businesses such as TaskRabbit, Thumbtack, Uber, DogVacay, Airbnb and WeWork are examples	(78–84)
Smart cities	Urban spaces leveraged with the use of technology focused on improving the living conditions of citizens or inhabitants	(80, 85–110)

In this section, all methods applied in this study are explained to ensure research replication and allow validity and reliability confirmation (111, 112). Also, in order to establish an acceptable degree of reliability in the research, the data analyses were triangulated (112) through different methods and techniques as necessary for social science literature reviews (113) to provide a consensus regarding the proposed capabilities list: traditional literature review, basic content analysis, grey literature mapping and experts review and confirmation (proposed model presentation and conformity verification), thus reducing the risks of common biases from inaccurate or selective observations and overgeneralisation (114), as shown in Figure 2. Details for each step are presented below.

Fig. 2.

Research methods applied to the present study

3.1 Data Collection: Scientific Papers

Data collection from scientific databases consisted in two basic steps: data source identification and data extraction criteria definition.

Although some previous published research used only one database source, for this study we combined data from two relevant and robust databases. The first was Scopus^®, which is considered to be the largest abstract and citation database of peer-reviewed literature, while the second independent and unbiased database was Web of Science^TM, known as one of the largest citation databases available and the first in the market. Both provide significant results for English-language journals according to comparative studies (115) and are very consistent with each other (116).

The same query logic was applied for both databases, along with the same filters and constraints, following the recommendations found in a previous published study, thus using similar expressions and precautions with specific taxonomies (19). Query logic for both CE and big data and IoT expressions are shown in Figure 3 and were applied for document title, keywords or abstract. Coding of key terms and themes to represent both CE and big data or IoT on database queries were obtained from previous research (19) in the absence of a comprehensive taxonomy and are reproduced in Appendix 5. Coding categories criteria are presented in Appendix 6 and the complete and detailed results in Appendix 7. After running the independent queries individually for both databases, the results were combined, generating an integrated result of 370 unique documents for analysis. At this point, no restrictions to document types or relevancy had been applied. Step two consisted of applying the authors’ analysis to eliminate incoherent documents. In order to avoid author biases during this phase, objective criteria for document elimination were defined: items retrieved from keywords or abstract but with no direct relation to document contents (for example, abstract mentioning, but document not about big data – term appears in abstract but is not related to it); term appears in document body but as a future research recommendation or indirect implication; namesake term used (such as ‘blue economy’). A total of 110 documents were removed from the set after reading. This represented an improvement from previous research (19) that focused only on the bibliometrics part without applying authors’ detailed in-depth proofreading and review. Then, non-applicable items such as conference reviews, errata or documents with no content were also discarded, representing a total of 29 documents. Finally, a total of five documents not in English were removed. The final set of documents used in the research consisted of 226 documents. The complete filter process is presented in Figure 4.

Fig. 3.

Query logic for Scopus^® and Web of Science^TM, adapted from previous published research with the use of the same lists of terms (19)

Fig. 4.

CE and big data or IoT documents search summary.

(^aNon-related documents: items containing the query keywords but with contents not related to the research subject)

Previous literature review research was consulted to try to identify other criteria to narrow the number of documents to be analysed to the most relevant. Cut-off methods based on scientific recognition were mapped (48, 117, 118), some of them applying Pareto principles to focus on the most cited articles and author research relevance. Nevertheless, as shown in Figure 1, most of the papers retrieved were less than two years old, so relying on scientific recognition by number of citations could have produced undesirable results. Because of this the authors decided to analyse the entire set of articles (226 documents) for this research.

3.2 Scientific Literature Review

Documents were classified according to the following criteria: country and region (Scopus^® and Web of Science^TM databases do not retrieve country names. Documents were assigned to countries according to (in this order of priority): author affiliation, main author affiliation, conference location, journal location or source title location, using the same criteria applied in prior research (19)); methodology type, in compliance with similar literature review research (119), composed of: (a) theoretical and conceptual papers, (b) case studies, (c) surveys, (d) modelling papers and (e) literature reviews; industry, according to the Standard Industrial Classification (SIC) codes assigned by the US government to business establishments to identify their primary business (120); and related CE principle according to the classification mapped for this research (20), divided into: (a) design, (b) reduction, (c) reuse, (d) recycle, (e) reclassification and (f) renewable energy.

Due to the considerable number of documents used in the review (226 after initial screening), the complete list with corresponding classifications is available in Appendix 7.

3.3 Triangulation: Content Analysis with Word Cloud

Word cloud is a tool that generates a visualisation in which the more frequently used words in a given text are highlighted. Although it provides good presentation and is visually appealing, it does not provide useful information when applied alone, but can perform well as a supplementary tool to help confirm the findings and related interpretations (121). So to support the research results confirmation, all 226 documents selected were converted into a robust text corpus and went through data mining with the support of ‘R’ statistical tool (122), so that expressions of more occurrences were ranked.

In order for the analysis to be accurate, compound expressions (bigrams, trigrams and four-grams) were bound together into single words prior to word cloud execution. Despite the existence of formal methods and patents for automated compound expressions generation (123), the authors decided to create the database manually due to the heterogeneity of subjects under analysis (i.e. CE, big data, IoT), so automatic conversion risks were avoided. The complete list is available in Appendix 4.

The authors then cleansed the results according to the following steps: (a) concatenation of expressions (for example, big data to bigdata); (b) unification of same meaning of words (for example, recycling and recycled for recycle); (c) separation of similar word with different meanings (building not the same as build); (d) removal of punctuation, numbers, URLs; (e) case conversion; (f) singularisation (for example, feet unified with foot); and (g) removal of stop words (function words such as ‘which’, ‘the’, ‘is’, ‘in’, verbs and auxiliary words) based on International Organization for Standardization (ISO) and snowball sources (124), combined with a customised list compiled by the authors and also shown in Appendix 4. The word cloud image was also generated with ‘R’. The following libraries were used in the analysis: ggplot2 (125), githubinstall (126), pluralise (127), RWeka (128, 129), SnowballC (124), stopwords (130), tm (131, 132), wordcloud (132).

3.4 Grey Literature Mapping

There are a number of non-academic institutions, such as government agencies, private businesses and non-governmental organisations (NGO) developing successful practical CE initiatives that need to be taken into consideration as both the subject matters – of CE and big data or IoT – are still emerging and evolving scientifically. Finding literature and information on this particular area of research required the use of non-scientific sources (134). Moreover, recent studies indicate that there are benefits for including grey literature in reviews: overall findings enrichment, bias reduction and to address stakeholders’ concerns (135), which are all relevant for this research. Furthermore, there is known to be a gap between the academic world and practitioners for this research subject (19).

The complete list of supplementary grey literature sources used to enrich the analysis is presented in Appendix 3.

3.5 Triangulation: Experts Review

The resulting preliminary framework was submitted to a group of eight domain experts who individually analysed the capabilities to assess the content clarity and representativeness, and to provide insights on items that could be revised or added to the list so that the authors could map additional research sources to be studied. The domain experts were selected first according to methods presented in the literature: type of knowledge, type of service and type of expertise (136). After identifying the experts, accessibility was considered as a second filter. A few conflicts identified were addressed with additional grey literature confirmation and were considered positive as they are common and important in social sciences (137). Expert contributions not verified in the literature were discarded. The list of domain experts is presented in Appendix 2.

Part II (34) will describe the results, conclusions and future recommendations of this research.

By Arc Consulting|2019-11-13T16:00:31+00:00November 13th, 2019|Weld Engineering Services|Comments Off

Artificial intelligence (AI) is a game-changing technology that is affecting all our lives and shaping our future. In the latest ISOfocus issue, we debunk the AI myths, explore the opportunities and explain why globally relevant standards are key.

Are killer robots about to take over the world? Mention artificial intelligence to the average person today and this is one of the many scary scenarios that spring to mind. Perhaps this is no surprise when you consider how AI is the technology that enables computers to think and act like human beings. But how much do we really know about this ground-breaking technology?

The November/December 2019 issue of ISOfocus separates the facts from the fiction and analyses the risks as well as the opportunities, illustrating how we all stand to gain as the technology develops over the coming years. The speed of change thus far has been startling and unsettling, full of innovators and disruptors. As S. Joe Bhatia, President and CEO of the American National Standards Institute (ANSI), ISO’s member for the USA, points out in his opening comment: “To help unlock the promise of AI and remove barriers to its adoption, globally relevant and globally accepted voluntary consensus standards are key.”

In this issue, experts unpick some of the myths surrounding AI and highlight the myriad potential gains for society – from healthcare and smart manufacturing, to robotics and intelligent transport systems, to name but a few. In an interview, Wael&nbspWilliam Diab, Chair of the subcommittee working on artificial intelligence, explains that AI is, in fact, not a single technology but represents an entire ecosystem, from collecting and sourcing data to deriving insights and implementing actions.

AI data is at the intersection of many different fields, and the magazine points out the need for a common framework so that consumers, producers and regulators can speak a common language. Leading experts delve into the issues of trust, safety and security, and explain how International Standards will help create an ethical foundation for building and using AI systems in the future.

“This is a pivotal time to be involved in standards for AI,” S. Joe Bhatia writes. Find out how ISO standards can help meet the challenges of artificial intelligence and make the world a better place by reading the latest ISOfocus.

By Arc Consulting|2019-11-11T08:14:56+00:00November 11th, 2019|Weld Engineering Services|Comments Off

To ethicize or not to ethicize…

Ethical decision making isn’t just another form of problem solving. As artificial intelligence (AI) grows in capability and influence, experts are treading uncharted territory to develop the International Standards for ethical AI, addressing its challenges from the onset.

Waymo began as the Google Self-Driving Car Project in 2009.

As algorithms become more sophisticated and autonomous, there is a risk that they begin to make important decisions on our behalf. The technology is already capable of automating decisions, such as medical diagnostics or smart manufacturing, that would normally be done by human beings.

When it comes to artificial intelligence, automotive technology is ahead of the curve. Autonomous cars are a popular research domain in AI. Big names such as Google, Uber and Tesla have been investigating how to make cars learn to drive correctly using deep reinforcement learning, i.e. learning by trial and error. But self-learning machines are vulnerable to failure, bringing ethical considerations to the fore. This challenges the conventional conception of moral responsibility: Who is responsible? And who holds the key to best practice?

In the absence of a global standard on AI, how do we bring more awareness about such issues? ISO and the International Electrotechnical Commission (IEC) have launched a range of work items through their joint technical subcommittee ISO/IEC JTC 1/SC 42, Artificial intelligence. Here, Mikael Hjalmarson of SIS, ISO’s member for Sweden, a leading expert in SC 42, explains how International Standards will help create an ethical foundation for building and using AI systems in the future.

ISOfocus: Techniques such as AI promise to be very transformative. Why are ethical and societal problems necessary considerations for AI?

Mikael Hjalmarson of SIS, ISO’s member for Sweden, is a leading expert in subcommittee ISO/IEC JTC 1/SC 42.

Mikael Hjalmarson: AI uses technologies that enable information to be collected and processed in new ways and more automatically. Nowadays, the capacity to handle a lot more data than in the past has increased – a potential that is prone to have ethical and societal consequences. It is when the data is managed in the hidden layers of an AI network, such as a neural or machine-learning implementation, that ethical and societal issues – which need not always be negative! – have to be considered. That is to say, the decisions and considerations that were previously handled outside the systems now have to be dealt with within the systems. It may also be that an AI application, no matter how “self-learning” it is, has preconceived biases that were inadvertently added when we developed and built the system.

It is imperative that we understand the ethical and societal considerations of the technology so that we can develop trustworthy systems that include assurances of transparency, explainability, bias mitigation, traceability, and so forth, as these are key to accelerating AI adoption and acceptance in the future. International Standards could play a role in identifying these ethical issues and provide the necessary framework to address them.

What are the biggest challenges facing AI ethics and societal issues? What are some of the consensus areas?

Robot cleaning vehicle with yellow body and cartoonish face photographed in Singapore's Changi Airport.

Robot helpers keep Singapore’s Changi Airport spick and span.

AI presents new and unique challenges to ethics. The main challenge is that systems leveraging AI can be implemented by many different users in different ways across various application verticals – from healthcare to mobility – with completely different requirements, and sometimes with market and regional differences as well. An AI technology becomes a “black box” that can answer questions… But can it tell you why one option is better than another? Can it provide alternatives? Then, there are the different policies, directives and environmental aspects to consider, for example those governing how data can and should be collected and used.

Another challenge is ensuring that aspects such as accountability, responsibility, trust, traceability and human values are handled equally (in the same way) so that they gain wide acceptance, even though we are not talking about creating value systems. An illustration of this might be that in one application domain it is permitted to capture and evaluate a given set of data while in another domain it is forbidden. For instance, a financial platform would be keen to avoid unintended bias rather than “AI eavesdropping” while healthcare would likely put the emphasis on transparency of the types of data captured. The system needs to be able to manage these differences.

WHAT TYPES OF ETHICAL AND SOCIAL STANDARDS IS SC 42 WORKING ON?

SC 42’s working group WG 3, Trustworthiness, is currently busy with a newly approved project. The idea is to collect and identify the ethical and societal considerations related to AI and link back to the trustworthiness projects we are working on. These efforts will culminate in a future technical report, ISO/IEC TR 24368, which aims to highlight the specifics of ethics and societal concerns in relation to other more generic ongoing projects on trustworthiness, risk management and bias, among others.

The ethics and societal aspects are examined from an ecosystem point of view, which could lead to more work being carried out in SC 42 in the future, as well as provide guidance to other ISO and IEC technical committees developing standards for domain-specific applications that use AI.

What are some of the regulatory issues in this area and how does SC 42 plan to overcome them? One of the challenges with ethical standards is that they are often voluntary, which means some AI technology creators may not follow them. Any thoughts?

Close-up of a navigation tablet on a motorcycle.

ISO, IEC and JTC 1 develop voluntary consensus standards across the board, not just on ethics. Our concern right now is that the technology is changing faster than regulators can keep up. This results in a cat-and-mouse game between the increasing use of AI in various types of systems and environments and the development of rules and legislation to control it. Since we are looking at the entire ecosystem, we have a cross-sector participation that represents the concerns of a variety of viewpoints in the field, including regulatory requirements.

One illustration of this is the navigation system in your car. It is perfectly acceptable for a GPS giving directions on the best route to get from A to B to go wrong from time to time, as we will probably still reach our destination eventually. But is it OK if an AI application chooses to give a patient a more effective drug (A) with a higher risk of side effects rather than the less effective drug (B) with fewer chances of side effects? This may work well in a hospital that has patients under control and doctors on site, but may be more inconvenient, not to mention risky, in an elderly care home. Had the drug been prescribed by a doctor, it would have been possible to ask why the more effective drug (A) was chosen, but an AI application that is only supposed to deliver a drug may not even be able to answer why drug (A) is more appropriate than drug (B).

International Standards, including those dealing with ethics, could serve as guidance to assist regulators in their work. For example, when building new systems that will be connected to other new or existing systems, they will increase the possibility of these being widely adopted and used. Standards, by their very nature, are developed for the long term; yet, today, the call is often for research and development, which means other types of documents may be needed on an ongoing basis. Alongside the new ethics project, a vocabulary establishing clear terms and definitions would therefore be a valuable asset to ensure a common understanding across the various parties involved and a good starting point for developing such documents.

When is it unethical not to use AI?

This is a difficult question because what is ethical or unethical very much depends on the context in which AI is used, which may also differ between regions. For instance, not using AI in the study of diseases could be deemed unethical since its very use might have increased the ability of finding a cure faster than if it had not been used.

It is important to remember the potential of AI to help solve some of our biggest challenges, in particular when they relate to human safety. But it’s a difficult judgement call. For example, is it more “ethically acceptable” when a self-driving car kills a number of individuals per year than when we humans are driving? Such is the dilemma of AI ethics.

Small self-driving yellow bus on the road in Berlin.

Berlin tests its first driverless buses in August 2019.

By Arc Consulting|2019-11-11T07:57:10+00:00November 11th, 2019|Weld Engineering Services|Comments Off