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OpenAI may have rehired Sam Altman, but AI needs more accountability, not ‘geniuses’

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by Joanne Gray

The OpenAI employees had faith in Altman. They believed in his vision and they did not like that the board could dismiss him so easily. Is their upset justified? Did the board overstep its bounds? Or did it exercise a necessary check on power?

by Joanne Gray

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The sudden removal of OpenAI CEO Sam Altman on Friday was met with shock and disapproval by the company’s employees. More than 90% signed a letter threatening to leave OpenAI if the board didn’t resign and reinstate Altman. The OpenAI employees had faith in Altman. They believed in his vision and they did not like that the board could dismiss him so easily. Is their upset justified? Did the board overstep its bounds? Or did it exercise a necessary check on power? https://twitter.com/satyanadella/status/1726509045803336122?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1726509045803336122%7Ctwgr%5E53a5ba6d82ed8a383027570f3ecdffc60d632db6%7Ctwcon%5Es1_&ref_url=https%3A%2F%2Ftheconversation.com%2Fopenais-board-is-facing-backlash-for-firing-ceo-sam-altman-but-its-good-it-had-the-power-to-218154 Silicon Valley’s ‘genius founder’ mythology The idea of a “genius founder” lies at the heart of Silicon Valley culture. Steve Jobs, Elon Musk, Mark Zuckerberg, Sergey Brin and Larry Page are not known as privileged men who managed to build successful businesses through a combination of hard work, smart decision-making and luck. Rather, they are celebrated as geniuses, wunderkinds, perhaps even maniacs – but always brilliant. Men who accomplished feats no one else could, because of their innate genius. A captivating founder narrative has become almost a prerequisite for any tech startup in Silicon Valley. It makes a company easier to sell and also structures power within the organisation. Throughout human history, founder mythologies have been used to explain, justify and sustain hierarchies of power. From heroes to deities to founding fathers, the founder myth provides a way to understand the current distribution of power and to unite around a figurehead. What happened this week at OpenAI was a challenge to the natural order of things in Silicon Valley. What happened to Sam? It’s quite remarkable a superstar “genius founder” such as Sam Altman wasn’t safeguarded by a company structure that could prevent his ousting. Tech company founders often create intricate structures to entrench themselves in their companies. For instance, when Google restructured into Alphabet, it created three share classes: one with standard voting rights, another with ten times the voting rights for the founders, and a third class without voting rights, mainly…

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Ethics

Could Counterfactuals Explain Algorithmic Decisions Without Opening the Black Box?

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by David Sutcliffe

Exploring the role of algorithms in our everyday lives, and how a “right to explanation” for decisions might be achievable in practice

by David Sutcliffe

Algorithmic systems (such as those deciding mortgage applications, or sentencing decisions) can be very difficult to understand, for experts as well as the general public. Image: Ken Lane (CC BY-NC 2.0).

The EU General Data Protection Regulation (GDPR) has sparked much discussion about the “right to explanation” for the algorithm-supported decisions made about us in our everyday lives. While there’s an obvious need for transparency in the automated decisions that are increasingly being made in areas like policing, education, healthcare and recruitment, explaining how these complex algorithmic decision-making systems arrive at any particular decision is a technically challenging problem—to put it mildly. In their article “Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR” which is forthcoming in the Harvard Journal of Law & Technology, Sandra Wachter, Brent Mittelstadt, and Chris Russell present the concept of “unconditional counterfactual explanations” as a novel type of explanation of automated decisions that could address many of these challenges. Counterfactual explanations describe the minimum conditions that would have led to an alternative decision (e.g. a bank loan being approved), without the need to describe the full logic of the algorithm. Relying on counterfactual explanations as a means to help us act rather than merely to understand could help us gauge the scope and impact of automated decisions in our lives. They might also help bridge the gap between the interests of data subjects and data controllers, which might otherwise be a barrier to a legally binding right to explanation. We caught up with the authors to explore the role of algorithms in our everyday lives, and how a “right to explanation” for decisions might be achievable in practice: Ed: There’s a lot of discussion about algorithmic “black boxes” — where decisions are made about us, using data and algorithms about which we (and perhaps the operator) have no direct understanding. How prevalent are these systems? Sandra: Basically, every decision that can be made by a human can now be made by an algorithm, which can be a good thing. Algorithms (when we talk about artificial intelligence) are very good at spotting patterns and…

Algorithmic systems (such as those deciding mortgage applications, or sentencing decisions) can be very difficult to understand, for experts as well as the general public. Image: Ken Lane (CC BY-NC 2.0).

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