{"id":67919,"date":"2023-04-20T17:13:36","date_gmt":"2023-04-20T21:13:36","guid":{"rendered":"https:\/\/world.edu\/?p=67919"},"modified":"2023-04-20T17:13:36","modified_gmt":"2023-04-20T21:13:36","slug":"ai-has-social-consequences-but-who-pays-the-price-tech-companies-problem-with-ethical-debt","status":"publish","type":"post","link":"https:\/\/world.edu\/ai-has-social-consequences-but-who-pays-the-price-tech-companies-problem-with-ethical-debt\/","title":{"rendered":"AI has social consequences, but who pays the price? Tech companies\u2019 problem with \u2018ethical debt\u2019"},"content":{"rendered":"

\"\"<\/p>\n

As public concern about the ethical and social implications of artificial intelligence keeps growing, it might seem like it\u2019s time to slow down. But inside tech companies themselves, the sentiment is quite the opposite. As Big Tech\u2019s AI race heats up, it would be an \u201cabsolutely fatal error in this moment to worry about things that can be fixed later,\u201d a Microsoft executive wrote in an internal email about generative AI,\u00a0as The New York Times reported<\/a>.<\/p>\n

In other words, it\u2019s time to \u201cmove fast and break things,\u201d\u00a0to quote Mark Zuckerberg\u2019s<\/a>\u00a0old motto. Of course, when you break things, you might have to fix them later \u2013 at a cost.<\/p>\n

In software development, the term \u201ctechnical debt<\/a>\u201d refers to the implied cost of making future fixes as a consequence of choosing faster, less careful solutions now. Rushing to market can mean releasing software that isn\u2019t ready, knowing that once it does hit the market, you\u2019ll\u00a0find out what the bugs are<\/a>\u00a0and can hopefully fix them then.<\/p>\n

However, negative news stories about generative AI tend not to be about these kinds of bugs. Instead, much of the concern is about AI systems amplifying\u00a0harmful biases and stereotypes<\/a>\u00a0and\u00a0students using AI<\/a>\u00a0deceptively. We hear about\u00a0privacy concerns<\/a>,\u00a0people being fooled by misinformation<\/a>,\u00a0labor exploitation<\/a>\u00a0and fears about how quickly\u00a0human jobs may be replaced<\/a>, to name a few. These problems are not software glitches. Realizing that a technology reinforces oppression or bias is very different from learning that a button on a website doesn\u2019t work.<\/p>\n

As\u00a0a technology ethics educator and researcher<\/a>, I have thought a lot about these kinds of \u201cbugs.\u201d What\u2019s accruing here is not just technical debt, but\u00a0ethical debt<\/a>. Just as technical debt can result from limited testing during the development process, ethical debt results from not considering possible negative consequences or societal harms. And with ethical debt in particular, the people who incur it are rarely the people who pay for it in the end.<\/p>\n

Off to the races<\/strong><\/h4>\n

As soon as OpenAI\u2019s ChatGPT\u00a0was released in November 2022<\/a>, the starter pistol for today\u2019s AI race, I imagined the debt ledger starting to fill.<\/p>\n

Within months, Google and Microsoft released their own generative AI programs, which seemed rushed to market in an effort to keep up. Google\u2019s stock prices fell when its chatbot Bard confidently\u00a0supplied a wrong answer<\/a>\u00a0during the company\u2019s own demo. One might expect Microsoft to be particularly cautious when it comes to chatbots, considering Tay, its Twitter-based bot that was\u00a0almost immediately shut down in 2016<\/a>\u00a0after spouting misogynist and white supremacist talking points. Yet early conversations with the AI-powered Bing\u00a0left some users unsettled<\/a>, and it\u00a0has repeated known misinformation<\/a>.<\/p>\n

When the social debt of these rushed releases comes due, I expect that we will hear mention of unintended or unanticipated consequences. After all, even with ethical guidelines in place, it\u2019s not as if OpenAI, Microsoft or Google can see the future. How can someone know what societal problems might emerge before the technology is even fully developed?<\/p>\n

The root of this dilemma is uncertainty, which is a common side effect of many technological revolutions, but\u00a0magnified in the case of artificial intelligence<\/a>. After all, part of the point of AI is that its actions are not known in advance. AI may not be designed to produce negative consequences, but it is designed to produce the unforeseen.<\/p>\n

However, it is disingenuous to suggest that technologists cannot accurately speculate about what many of these consequences might be. By now, there have been countless examples of how AI can reproduce bias and exacerbate social inequities, but these problems are rarely publicly identified by tech companies themselves. It was external researchers who found racial bias in widely used commercial\u00a0facial analysis systems<\/a>, for example, and in a\u00a0medical risk prediction algorithm<\/a>\u00a0that was being applied to around 200 million Americans. Academics and advocacy or research organizations like the\u00a0Algorithmic Justice League<\/a>\u00a0and the\u00a0Distributed AI Research Institute<\/a>\u00a0are doing much of this work: identifying harms after the fact. And this pattern doesn\u2019t seem likely to change if\u00a0companies keep firing ethicists<\/a>.<\/p>\n

Speculating \u2013 responsibly<\/strong><\/h4>\n

I sometimes describe myself as\u00a0a technology optimist<\/a>\u00a0who thinks and prepares like a pessimist. The only way to decrease ethical debt is to take the time to think ahead about things that might go wrong \u2013 but this is not something that technologists are necessarily\u00a0taught to do<\/a>.<\/p>\n

Scientist and iconic science fiction writer Isaac Asimov<\/a>\u00a0once said that sci-fi authors \u201cforesee the inevitable, and although problems and catastrophes may be inevitable, solutions are not.\u201d Of course, science fiction writers do not tend to be tasked with developing these solutions \u2013 but right now, the technologists developing AI are.<\/p>\n

So how can AI designers learn to think more like science fiction writers?\u00a0One of my current research projects<\/a>\u00a0focuses on developing ways to support this process of ethical speculation. I don\u2019t mean designing with far-off robot wars in mind; I mean the ability to consider future consequences at all, including in the very near future.<\/p>\n

This is a topic\u00a0I\u2019ve been exploring<\/a>\u00a0in my teaching for some time, encouraging students to think through the ethical implications of sci-fi technology in order to prepare them to do the same with technology they might create. One exercise I developed is called the\u00a0Black Mirror Writers Room<\/a>, where students\u00a0speculate about possible negative consequences<\/a>\u00a0of technology like social media algorithms and self-driving cars. Often these discussions are based on\u00a0patterns from the past or the potential for bad actors<\/a>.<\/p>\n

Ph.D. candidate\u00a0Shamika Klassen<\/a>\u00a0and I evaluated this teaching exercise in a\u00a0research study<\/a>\u00a0and found that there are pedagogical benefits to encouraging computing students to imagine what might go wrong in the future \u2013 and then brainstorm about how we might avoid that future in the first place.<\/p>\n

However, the purpose isn\u2019t to prepare students for those far-flung futures; it is to teach speculation as a skill that can be applied immediately. This skill is especially important for helping students\u00a0imagine harm to other people<\/a>, since technological harms often disproportionately impact marginalized groups that are\u00a0underrepresented in computing professions<\/a>. The next steps for my research are to translate these ethical speculation strategies for real-world technology design teams.<\/p>\n

Time to hit pause?<\/strong><\/h4>\n

In March 2023,\u00a0an open letter<\/a>\u00a0with thousands of signatures advocated for pausing training AI systems more powerful than GPT-4. Unchecked, AI development \u201cmight eventually outnumber, outsmart, obsolete and replace us,\u201d or even cause a \u201closs of control of our civilization,\u201d its writers warned.<\/p>\n

As\u00a0critiques of the letter<\/a>\u00a0point out, this focus on hypothetical risks ignores actual harms happening today. Nevertheless, I think there is little disagreement among AI ethicists that AI development needs to slow down \u2013 that developers throwing up their hands and citing \u201cunintended consequences\u201d is not going to cut it.<\/p>\n

We are only a few months into the \u201cAI race\u201d picking up significant speed, and I think it\u2019s already clear that ethical considerations are being left in the dust. But the debt will come due eventually \u2013 and history suggests that Big Tech executives and investors may not be the ones paying for it.<\/p>\n

Author Bio: <\/strong>Casey Fiesler is Associate Professor of Information Science at the University of Colorado Boulder<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

As public concern about the ethical and social implications of artificial intelligence keeps growing, it might seem like it\u2019s time to slow down. But inside tech companies themselves, the sentiment is quite the opposite. As Big Tech\u2019s AI race heats up, it would be an \u201cabsolutely fatal error in this moment to worry about things […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[85],"tags":[434399],"coauthors":[11733],"class_list":["post-67919","post","type-post","status-publish","format-standard","hentry","category-social-change","tag-social-implications-of-artificial-intelligence"],"acf":[],"yoast_head":"\nAI has social consequences, but who pays the price? 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