Archive for power to the machines

même pas peur [not afrAId]

Posted in Books, Kids, Travel, University life with tags , , , , , , , , , , , , , on March 30, 2023 by xi'an

Both the Beeb and The New York Times are posting tonight about a call to pause AI experiments, by AI researchers and others, due to the danger they could pose to humanity. While this reminds me of Superintelligence, a book by Nick Bostrom I found rather unconvincing, and although I agree that automated help-to-decision systems should not become automated decision systems, I am rather surprised at them setting the omnipresent Chat-GPT as the reference not to be exceeded.

“AI systems with human-competitive intelligence can pose profound risks to society and humanity (…) recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control.”

The central (?) issue here is whether something like Chat-GPT can claim anything intelligence, when pumping data from a (inevitably biased) database and producing mostly coherent sentences without any attention to facts. Which is useful when polishing a recommendation letter at the same level as a spelling corrector (but requires checking for potential fake facts inclusions, like imaginary research prizes!)

“Contemporary AI systems are now becoming human-competitive at general tasks, and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization?”

The increasingly doom-mongering tone of the above questions is rather over the top (civilization, nothing less?!) and again remindful of Superintelligence, while spreading propaganda and untruth need not wait super-AIs to reach conspiracy theorists.

“Such decisions must not be delegated to unelected tech leaders. Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable (…) Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in”

A six months period sounds like inappropriate for an existential danger, while the belief that governments want or can intervene sounds rather naïve, given for instance that they lack the ability to judge of the dangerosity of the threat and of the safety nets to be imposed on gigantic black-box systems. Who can judge on the positivity and risk of a billion (trillion?) parameter model? Why is being elected any guarantee of fairness or acumen? Beyond dictatures thriving on surveillance automata, more democratic countries are also happily engaging into problematic AI experiments, incl. AI surveillance of the incoming Paris Olympics. (Another valuable reason to stay away from Paris over the games.)

“AI research and development should be refocused on making today’s powerful, state-of-the-art systems more accurate, safe, interpretable, transparent, robust, aligned, trustworthy, and loyal. In parallel, AI developers must work with policymakers to dramatically accelerate development of robust AI governance systems.”

While these are worthy goals, at a conceptual level—with the practical issue of defining precisely each of these lofty adjectives—, and although I am certainly missing a lot from my ignorance of the field,  this call remains a mystery to me as it sounds unrealistic it could achieve its goal.

algorithm for predicting when kids are in danger [guest post]

Posted in Books, Kids, Statistics with tags , , , , , , , , , , , , , , , , , on January 23, 2018 by xi'an

[Last week, I read this article in The New York Times about child abuse prediction software and approached Kristian Lum, of HRDAG, for her opinion on the approach, possibly for a guest post which she kindly and quickly provided!]

A week or so ago, an article about the use of statistical models to predict child abuse was published in the New York Times. The article recounts a heart-breaking story of two young boys who died in a fire due to parental neglect. Despite the fact that social services had received “numerous calls” to report the family, human screeners had not regarded the reports as meeting the criteria to warrant a full investigation. Offered as a solution to imperfect and potentially biased human screeners is the use of computer models that compile data from a variety of sources (jails, alcohol and drug treatment centers, etc.) to output a predicted risk score. The implication here is that had the human screeners had access to such technology, the software might issued a warning that the case was high risk and, based on this warning, the screener might have sent out investigators to intervene, thus saving the children.

These types of models bring up all sorts of interesting questions regarding fairness, equity, transparency, and accountability (which, by the way, are an exciting area of statistical research that I hope some readers here will take up!). For example, most risk assessment models that I have seen are just logistic regressions of [characteristics] on [indicator of undesirable outcome]. In this case, the outcome is likely an indicator of whether child abuse had been determined to take place in the home or not. This raises the issue of whether past determinations of abuse– which make up  the training data that is used to make the risk assessment tool–  are objective, or whether they encode systemic bias against certain groups that will be passed through the tool to result in systematically biased predictions. To quote the article, “All of the data on which the algorithm is based is biased. Black children are, relatively speaking, over-surveilled in our systems, and white children are under-surveilled.” And one need not look further than the same news outlet to find cases in which there have been egregiously unfair determinations of abuse, which disproportionately impact poor and minority communities.  Child abuse isn’t my immediate area of expertise, and so I can’t responsibly comment on whether these types of cases are prevalent enough that the bias they introduce will swamp the utility of the tool.

At the end of the day, we obviously want to prevent all instances of child abuse, and this tool seems to get a lot of things right in terms of transparency and responsible use. And according to the original article, it (at least on the surface) seems to be effective at more efficiently allocating scarce resources to investigate reports of child abuse. As these types of models become used more and more for a wider variety of prediction types, we need to be cognizant that (to quote my brilliant colleague, Josh Norkin) we don’t “lose sight of the fact that because this system is so broken all we are doing is finding new ways to sort our country’s poorest citizens. What we should be finding are new ways to lift people out of poverty.”