trust in ai: the role of hybrid and private governance & challenges · asun lera st. clair 12...

DNV GL © 12 September 2019 SAFER, SMARTER, GREENERDNV GL ©

Asun Lera St. Clair 12 September 2019

Joint Viros-Signal Symposium

Trust in AI: The role of hybrid and private governance & challengesJoint SiGNAL and VIROS WorkshopUiO – Faculty of Law

DNV GL © 2018 12 September 2019

Everybody talks about Trust (or not) in AI

A good example of the societal scrutiny of technology

– Very fast implementation of the technologies

– Governance gaps

– Challenges governance systems and processes

Debate on the AI ethics

Less visible

– AI Safety

– AI in society

– Leveraging AI for social and environmental challenges (e.g., climate change, SDGs, or humanitarian assistance)

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Conditions for trust from an industry perspective

World Economic Forum:

– Bias

– Transparency

– Accountability

– Privacy

Microsoft:

– Fairness

– Reliability and safety

– Privacy and security

– Inclusiveness

IBM:

– Fairness

– Robustness

– Explainability

– Lineage

A rush for adapting or creating standards and verification methods for:

– Trust in data

– Trust in models and their assumptions

– Trust in algorithmic agency

– Trust in self-learning algorithms

– Computational ethics

• Explainable AI• Emerging as a filed

on its own

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Ethics & Trust in multilateral organisations

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“Trustworthy AI has three components, which should be met throughout the system's entire life cycle: (1) it should be lawful, complying with all applicable laws and regulations (2) it should be ethical, ensuring adherence to ethical principles and values and (3) it should be robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm.”


Moral machines? Ethical coders?

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Would you be a utilitarian or a human rights defender?

Created as a thought exercise in metaethics to illustrate differences in ethical theories

DNV GL © 11 March 2019

Poor understanding of public perception

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Source: Cave, S., and K.Dihal (2019), Hopes and fears for intelligent machines in fiction and reality, Nature


On trust

Trust underlies all personal, social, institutional and economic relations, providing shared values and shared goals. All industrial structures, political institutions and societies are glued together through diverse

forms of trust mechanisms (Fukuyama 1995).

Trust is a facilitator of relations, and it is this facilitator role what makes trust valuable.

Trust also facilitates relations between people and technologies (Taddeo 2017).

Thus trust is so pervasive, that ‘a complete absence of trust would prevent [one] even getting up in the morning (Luhmann 1979).’

But without an underlying set of shared values, trust is tentative, fragile.

And although digital technologies seem to enable trust through connectivity, and creates what Bostman calls distributed trust (Bostman 2017), there is a substantive amount of mistrust in AI, often because we are asked for blind trust.

Source: St. Clair et al 2019

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Institutions that enable good judgment

Trust is not a matter of blind deference but rather a question of “placing-or refusing-trust with good judgment” (O’Neill 2002)

Societies have institutions that enable such judgments

– Institutions are not bureaucracies but rather stable, valued and recurring patterns of behaviour, mechanisms for social order with a social purpose (Huntington 1968).

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AI governance is then about creating institutions to ensure safe and responsible AI & intrinsically linked to ethics

Three common features of governance are:

– It is concerned with realisingpublic goals, through the process of steering a particular constituency of actors, and is regarded as authoritative.

(Andonova, Betsill and Bulkeley2009)

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Private (hybrid) governance

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We refer to private governance when rules, and compliance with those rules, are set by a range of non-governmental bodies, such as industry associations, NGOs, networks of firms, or technical experts, often in collaboration with government

Business transactions are layered and mediated by both private and hybrid accountability mechanisms

– Standards, certification, verification

These self-regulate the behavior of companies, align business practices with widely held ethical principles, and enable societal trust.

Private rules thus govern—that is, they enable and constrain—a broad range of activities in the world economy (Büthe 2010).

NB: Historical relations of third party organizations to insurance


A key private governance mechanism: Standards

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(St.Clair and Aalbu 2017)


One should not romanticize private governance… nor under-estimate it

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Standards “have a way of sinking below the level of social visibility, eventually becoming part of the taken-for-granted technical and moral infrastructure of modern life.”

Source: Timmermans and Epstein, 2010: 71


Challenges to existing private governance regimes

Assurance refers to the validation and confidencethat a product or process is fit for purpose, complies with existing safety or other technical requirements or regulations.

Based on credible technical information or knowledge.

Identifies what is or not an acceptable and insurable risk.

But, the deployment of AI may change the nature and the way in which we assure and audit today

It may also require changes in the existing governance of many industries

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A key challenge is the complex nature of intelligent systems

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Cyberphysical systems (robotics) are very different from physical systems, we need new methods tools and frameworks to assure them

They are complex systems with emergent properties, difficult to establish a priori.

This means we need digital tools to test the systems, for example, simulation, digital twins, sharing of data across components of the system etc.

An ecosystem for collaborative digital twin simulations to solve challenges with designing, commissioning, operating and assuring complex, integrated systems


Another challenge is the need to open the black boxes of human-machine cobehaviour

The social conception of intelligent technologies

Sociology of AI

Unpack users of AI from design, operation, use, impact

Adaptive interaction

Agency and co-agency

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A call for alliances

We need understanding

– changes in existing systems for providing trust in technology

– how best should public governance, law and private regulation work together fast enough to provide an appropriate regulatory infrastructure.

We also need:

– a revolution in the way we approach the relations between technology and society

– Interdisciplinarity, domain and digital competence

– Public-private partnerships

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Asuncion Lera [email protected]

Twitter: @asunstclair+ 47 452 619 02

trust in ai: the role of hybrid and private governance & challenges · asun lera st. clair 12...

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