The relationship between trust in AI and trustworthy machine learning technologies

Toreini, Ehsan; Aitken, Mhairi; Coopamootoo, Kovila; Elliott, Karen; Zelaya, Carlos Gonzalez; Moorsel, Aad van

doi:10.1145/3351095.3372834

Cited by 189 publications

(120 citation statements)

References 57 publications

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“…To echo Kunneman ( 2010 : 132), addressing “complexity is not only the central scientific, but also the central ethical problem of our time”. We know that algorithms can reconstruct people’s data and discriminate around gender and semantics (Perez 2019 ; Toreini et al 2020 ). However, the subjective concept of “trustworthiness” to remove bias is difficult in computer science engineering discourse and practice; hence, we consider the complexities in defining and building trust, in light of the plethora of ethical principles in circulation.…”

Section: Vested Interestsmentioning

confidence: 99%

“…This does not compensate for the lack of real and meaningful sociotechnical interaction between internal and external “end user” stakeholders. DEAR underplays the importance of co-governance in stakeholder engagement to de-risk AI and hold it accountable to build trust (Ackerman 2004 ), in respect of bias, ethical and societal impacts, which ultimately lead to legal consequences for AI systems businesses (Coeckelbergh 2020 ; Toreini et al 2020 ). Such core elements are the heart of outcome-based risks associated with AI systems, which can and do undermine trust.…”

Section: Trust Ethics and Human Oversight Of Aimentioning

confidence: 99%

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Towards an Equitable Digital Society: Artificial Intelligence (AI) and Corporate Digital Responsibility (CDR)

Elliott

Price²,

Shaw³

et al. 2021

Soc

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In the digital era, we witness the increasing use of artificial intelligence (AI) to solve problems, while improving productivity and efficiency. Yet, inevitably costs are involved with delegating power to algorithmically based systems, some of whose workings are opaque and unobservable and thus termed the “black box”. Central to understanding the “black box” is to acknowledge that the algorithm is not mendaciously undertaking this action; it is simply using the recombination afforded to scaled computable machine learning algorithms. But an algorithm with arbitrary precision can easily reconstruct those characteristics and make life-changing decisions, particularly in financial services (credit scoring, risk assessment, etc.), and it could be difficult to reconstruct, if this was done in a fair manner reflecting the values of society. If we permit AI to make life-changing decisions, what are the opportunity costs, data trade-offs, and implications for social, economic, technical, legal, and environmental systems? We find that over 160 ethical AI principles exist, advocating organisations to act responsibly to avoid causing digital societal harms. This maelstrom of guidance, none of which is compulsory, serves to confuse, as opposed to guide. We need to think carefully about how we implement these algorithms, the delegation of decisions and data usage, in the absence of human oversight and AI governance. The paper seeks to harmonise and align approaches, illustrating the opportunities and threats of AI, while raising awareness of Corporate Digital Responsibility (CDR) as a potential collaborative mechanism to demystify governance complexity and to establish an equitable digital society.

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Section: Vested Interestsmentioning

confidence: 99%

Section: Trust Ethics and Human Oversight Of Aimentioning

confidence: 99%

Towards an Equitable Digital Society: Artificial Intelligence (AI) and Corporate Digital Responsibility (CDR)

Elliott

Price²,

Shaw³

et al. 2021

Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…Carmon et al (2019) Build trust in all stages in AI life cycle for ensuring fair and non-discriminatory consumer outcomes. Toreini et al (2019) Understand ways to balance between achieving organizational benefits of using AI and addressing dark sides of AI for gaining sustainable benefits.…”

Section: Implications and Directions For Future Researchmentioning

confidence: 99%

Addressing Algorithm Bias in AI-Driven Customer Management

2021

Journal of Global Information Management

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Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications.

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“…While developing trustworthy data practices remains an emerging field of interest, increasingly technical approaches are being developed with this aim (Toreini et al, 2019). For example, IBM (n.d.) has set out approaches towards 'building and enabling AI solutions people can trust' through four key features of "Trustworthy AI": Robustness, Fairness, Explainability and Lineage.…”

Section: Technical Approachesmentioning

confidence: 99%

Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices

et al. 2020

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Current attention directed at ethical dimensions of data and Artificial Intelligence have led to increasing recognition of the need to secure and maintain public support for uses (and reuses) of people's data. This is essential to establish a "Social Licence" for current and future practices. The notion of a "Social Licence" recognises that there can be meaningful differences between what is legally permissible and what is socially acceptable. Establishing a Social Licence entails public engagement to build relationships of trust and ensure that practices align with public values. While the concept of the Social Licence is well-established in other sectors-notably in relation to extractive industries-it has only very recently begun to be discussed in relation to digital innovation and data-intensive industries. This article therefore draws on existing literature relating to the Social Licence in extractive industries to explore the potential approaches needed to establish a Social Licence for emerging data-intensive industries. Additionally, it draws on well-established literature relating to trust (from psychology and organisational science) to examine the relevance of trust, and trustworthiness, for emerging practices in data-intensive industries. In doing so the article considers the extent to which pursuing a Social Licence might complement regulation and inform codes of practice to place ethical and social considerations at the heart of industry practice. We focus on one key industry: Financial Technology. We demonstrate the importance of combining technical and social approaches to address ethical challenges in data-intensive innovation (particularly relating to Artificial Intelligence) and to establish relationships of trust to underpin a Social Licence for Financial Technology. Such approaches are needed across all areas and industries of data-intensive innovation to complement regulation and inform the development of ethical codes of practice. This is important to underpin culture change and to move beyond rhetorical commitments to develop best practice putting ethics at the heart of innovation.

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The relationship between trust in AI and trustworthy machine learning technologies

Cited by 189 publications

References 57 publications

Towards an Equitable Digital Society: Artificial Intelligence (AI) and Corporate Digital Responsibility (CDR)

Towards an Equitable Digital Society: Artificial Intelligence (AI) and Corporate Digital Responsibility (CDR)

Addressing Algorithm Bias in AI-Driven Customer Management

Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices

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