{"id":980,"date":"2021-02-01T08:27:04","date_gmt":"2021-02-01T07:27:04","guid":{"rendered":"https:\/\/forhumanity.center\/bok\/auditing-ai-and-autonomous-systems-building-an-infrastructureoftrust\/"},"modified":"2021-02-01T08:27:04","modified_gmt":"2021-02-01T07:27:04","slug":"auditing-ai-and-autonomous-systems-building-an-infrastructureoftrust","status":"publish","type":"post","link":"https:\/\/forhumanity.center\/bok\/auditing-ai-and-autonomous-systems-building-an-infrastructureoftrust\/","title":{"rendered":"Auditing AI and Autonomous systems – building an #infrastructureoftrust"},"content":{"rendered":"

How do we build trust? How can we systematically assure trust in our systems? How would auditing AI and autonomous systems contribute to this goal?<\/em><\/strong><\/h4>\n

Explaining how the world\u2019s most famous accounting\/audit disaster – Enron – exemplifies the merits of Independent Audit, helps illustrate how a robust system of oversight is essential to building trust.<\/em><\/strong><\/h4>\n

Few would disagree with the statement \u201cWe need to be able to trust our artificial intelligence and autonomous systems\u201d. But achieving that trust \u2014 rather than merely desiring it \u2014 commands an understanding of what trust is: how is it achieved, what factors need to be in place in order for it to be assured? What processes, standards, behaviours \u2014 and accountability \u2014 needs to be guaranteed in order to confidently say: \u201cI trust this\u201d?<\/p>\n

This paper explores those questions by outlining key elements to building and preserving trust and exploring how these elements have and do apply to a range of industries and historic events.  It also suggests some solutions for building trust in AI and autonomous systems.  Solutions we have enough confidence in that we are beginning to operationalise them now.<\/p>\n

On its face, Enron, the world\u2019s most famous audit\/accounting disaster, might highlight the failure of auditing, and point to the struggles facing any attempt to audit AI and autonomous systems.  But that would be to look and not really see… <\/p>\n

The vast majority of audits do not uncover disasters, large scale frauds or malfeasance.    Indeed, thousands of public entities are audited without fanfare every year.  The Enron case  is an outlier of the most extreme kind, which, if studied correctly and reconciled with the 99.99% of regular audits, can inform what correct and robust structures are needed for an #infrastructureoftrust.<\/p>\n

By designing and creating Independent Audit of AI Systems (IAAIS), ForHumanity will help build a system of trust in our AIs and autonomous systems.  Trust has been studied by many for centuries, and there is no universal and perfect process that guarantees it. So, instead, we look for instances of trust and see which elements they have in common.  Here we submit some of the elements that consciously and subconsciously combine to allow us to trust:<\/p>\n

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  1. Predictability<\/strong> (a belief or perception about the likelihood of future outcomes from an interaction)<\/li>\n
  2. Transparency<\/strong> (access to information allowing for decision-making, especially changes from current trajectories)<\/li>\n
  3. Understanding <\/strong>(awareness of a sufficient level of information and knowledge to feel comfortable with a decision or choice)<\/li>\n
  4. Control<\/strong> (power equating to a sense of being in-charge and the belief that a decision has multiple viable and beneficial choices)<\/li>\n
  5. Security<\/strong> (protection from harm: physical, mental, spiritual, financial or emotional)<\/li>\n
  6. Fairness<\/strong> (the belief that the interaction has a balanced set of pros and cons for both\/all parties)<\/li>\n
  7. Equity<\/strong> (the belief that an individual\u2019s interaction is comparable to others with similar circumstances) <\/li>\n
  8. Morality<\/strong> (belief that the trusted party\/entity cares about their impact on us or at least they are not intending to harm people)<\/li>\n<\/ol>\n

    Each of us \u2018trusts\u2019 in different ways. Depending on the interaction in question, we ascribe more or less value to these listed attributes.  Monetary interactions might score lower on the Moral variable and higher on Fairness while trust in manual driving scores high on Predictability (if not, it would be nearly impossible to share a road).  If we are to build trust an #infrastructureoftrust that serves all people, then all of these elements must be accounted for in a robust way. <\/p>\n

    From this foundation, we tackled AIs and Autonomous Systems. We didn\u2019t have to start from scratch: in our purview was an existing #infrastructureoftrust with a nearly 50 year track record \u2014 the system of Independent Audit already well established in the world of Financial Reporting and Financial Accounting.  <\/p>\n

    We took the \u2018Trust Variables’ above and considered them in relation to the key three parties (auditor, compliant entity, society) engaged in an existing financial audit.<\/p>\n

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    1. Predictability<\/strong> \n