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Simion and Kelp on trustworthy AI

Simion and Kelp on trustworthy AI Simion and Kelp offer a prima facie very promising account of trustworthy AI. One benefit of the account is that it elegantly explains trustworthiness in the case of can- cer diagnostic AIs, which involve the acquisition by the AI of a representational eti- ological function. In this brief note, I offer some reasons to think that their account cannot be extended — at least not straightforwardly — beyond such cases (i.e., to cases of AIs with non-representational etiological functions) without incurring the unwanted cost of overpredicting untrustworthiness. Keywords Trustworthy AI · Trustworthiness · Etiological functionalism · Artificial intelligence 1 Introduction Increasingly, the question of whether — and if so under what conditions — artificial intelligence (AI) can be ‘trustworthy’ (as opposed to merely reliable or unreliable) is being debated by researchers across various disciplines with a stake in the matter, from computer science and medicine to psychology and politics. Given that the nature and norms of trustworthiness itself have been of longstand- ing interest in philosophy, philosophers of trust are well situated to help make pro- gress on this question. In their paper “Trustworthy Artificial Intelligence” (2023), Simion and Kelp (hereafter, S&K) aim to do just this. I think they largely succeed. That said, in this short note I am going to quibble with a few details. In short, I worry that their reliance on function-generated obli- gations in their account of trustworthy AI helps their proposal get exactly the right For some recent reviews, see, e.g., Kaur et al. (2022); and Kaur et al. (2020). See, e.g., Hardin (1996); Jones (2012); Frost-Arnold (2014); O’Neill (2018); Simion and Kelp (2023); Carter (2022). See also Carter and Simion (2020) for a review. * J. Adam Carter adam.carter@glasgow.ac.uk Reader in Philosophy, University of Glasgow, 67-69 Oakfield Avenue, Glasgow, Scotland, UK Vol.:(0123456789) 1 3 18 Page 2 of 8 Asian Journal of Philosophy (2023) 2:18 result in certain central AI cases, such as cancer diagnostic AIs, but at the potential cost of overpredicting untrustworthiness across a range of other AIs. Here’s the plan for the paper. In Sect.  2 I’ll provide a brief overview of S&K’s account of trustworthy AI, emphasising the core desiderata they take themselves to have met. In Sect. 3, I’ll then raise some potential worries, and discuss and critique some lines of reply. 2 S&K’s line of argument A natural strategy for giving an account of trustworthy AI will be a kind of ‘applica- tion’ strategy: (i) give a compelling account of trustworthiness simpliciter and then (ii) apply it to AI, and make explicit what follows, illuminating trustworthy AI in the process. But, as S&K note, there is a problem that faces many extant accounts of trustwor- thiness that might try to opt for that strategy. The problem is this: many accounts of trustworthiness are such that the psychological assumptions underlying them (e.g., that being trustworthy involves something like a good will or virtue) are simply too anthropocentric. As S&K ask: Do Als have something that is recognizable as goodwill? Can AIs host charac- ter virtues? Or, to put it more precisely, is it correct to think that AI capacity for trustworthiness co-varies with their capacity for hosting a will or character virtues? (p. 4). The situation seems to be this: an account of trustworthiness with strongly anthro- pocentric psychological features ‘baked in’ will either not be generalisable to AI (if AI lacks good will, virtue, etc.), or it will be generalisable only by those willing to embrace further strong positions about AI. Ceteris paribus, a more ‘generalisable’ account of trustworthiness, when it comes to an application to AI specifically, will be a less anthropocentric one that could sidestep the above problem. One candidate such account they identify is Hawley’s (2019) negative account of trustworthiness, on which trustworthiness is a matter of avoiding unfulfilled commitments. It is worth registering that some philosophers of trust might begin with the premise that trust and trust- worthiness are essentially anthropocentric; for instance, on the supposition that trust requires vulnerability to betrayal, one might think that one simply can’t be betrayed by artifacts or that which lacks agency. On this way of approaching the topic, Simion and Kelp are engaged with here, we might simply deflate talk of trustworthy AI to ‘reliable’ AI, and focus on conditions of rational reliance on AI. For my purposes here, however, I am assuming along with S&K, and also following an established research area on trustworthy autonomous systems, that in-principle extendability to AI things is desirable feature of a view of a philo- sophical view of trustworthiness. At any rate, in order to explore the interesting features of S&K’s article, I am not challenging the assumption they make that such extendability is not misguided. Another recent account of trustworthiness that is arguably not too anthropocentric that it would be dif- ficult to generalise over to AI is the performance-theoretic account of trustworthiness. See here, Carter (2022). 1 3 Asian Journal of Philosophy (2023) 2:18 Page 3 of 8 18 S&K have argued elsewhere at length for a different — and similarly non overtly anthropocentric — account of trustworthiness, which they take to have advantages (I won’t summarise these here) over Hawley’s: on S&K’s preferred account, trustwor- thiness is understood as a disposition to fulfil one’s obligations. What is prima facie attractive about an obligation-centric account of trustworthi- ness, for the purpose of generalising that account to trustworthy AI, is that (i) arti- facts can have functions; and (ii) functions can generate obligations. Let’s look at the first point first. S&K distinguish between design functions (d-functions), sourced in the designer’s intentions, and etiological functions (e-func- tions), sourced in a history of success, noting that artefacts can acquire both kinds of functions. S&K use the example of a knife to capture this point: My knife, for instance, has the design function to cut because that was, plausi- bly, the intention of its designer. At the same time, my knife also has an etio- logical function to cut: that is because tokens of its type have cut in the past, which was beneficial to my ancestors, and which contributes to the explana- tion of the continuous existence of knives. When artefacts acquire etiologi- cal functions on top of their design functions, they thereby acquire a new set of norms governing their functioning, sourced in their etiological functions. Design-wise, my knife is properly functioning (henceforth properly d-func- tioning) insofar as it’s working in the way in which its designer intended it to work. Etiologically, my knife is properly functioning (henceforth properly e-functioning) insofar as it works in a way that reliably leads to cutting in nor- mal conditions (p. 9). While d-functions and e-functions (i.e., proper functioning) will often line up, these functions can come apart (e.g., when artifacts are designed to work in non- e-function-filling ways). When they don’t line up, S&K maintain that e-functions generally override. As they put it: what we usually see in cases of divergence is that norms governing proper- functioning tend to be incorporated in design plans of future generations of tokens of the type: if we discover that there are more reliable ways for the arte- fact in question to fulfil its function, design will follow suit (Ibid., p. 9). So we have in view now S&K’s thinking behind the idea that artifacts (of which AI is an instance) can acquire functions. What about the next component of the view: that functions can generate obligations? The crux of the idea is that a species of obligation, function-generated obligation, is implicated by facts about what it is for something to fulfil its e-function. The heart has a purely e-function–generated obligation to pump blood in normal conditions (the conditions under which pumping blood contributed to explanation of its contin- ued existence). In maintaining this, on S&K’s line, we aren’t doing anything objec- tionably anthropocentric, any more than when we say a heart should (qua heart) See Kelp and Simion (2023). 1 3 18 Page 4 of 8 Asian Journal of Philosophy (2023) 2:18 pump blood. We can easily extend this kind of obligation talk over to artifacts, then: just as a heart is malfunctioning (and so not meeting its e-functionally sourced obli- gations) if it stops pumping blood, a diagnostic AI is malfunctioning (and not meet- ing its e-functionally sourced obligations) if it stops recognising simple tumours by their appearance, and miscategorises them. Against this background, then, S&K define an AI’s being maximally trustwor - thy at phi-ing as being a matter of having a “maximally strong disposition to meet its functional norms-sourced obligations to phi.” The conditions for outright AI trustworthiness attributions can then be characterised in terms of maximal AI trust- worthiness in the following way: an outright attribution of trustworthiness to an AI is true in a context c iff that AI approximates “maximal trustworthiness to phi” closely enough to surpass a threshold on degrees of trustworthiness determined by c, where the closer x approximates maximal trustworthiness to phi, the higher x’s degree of trustworthiness to phi. 3 Critical discussion I suspect that a typical place one might begin to poke to look for a hole in the above account would be the very idea that a machine could have an obligation in the first place. Imagine this line or reply: “But S&K have complained that extant accounts of trustworthiness that rely on ‘virtue’ and ‘good will’ as psychologically demand- ing prerequisites for being trustworthy are too anthropocentric to be generalisable to AI. But isn’t being a candidate for an ‘obligation’ equally psychologically demand- ing and thereby anthropocentric? If so, haven’t they failed their own generalisability desiderata by their own lights?”. The above might look superficially like the right way to press S&K, but I think such a line would be uncharitable, so much so that it’s not worth pursuing. First, we humans often have our own obligations to others sourced in facts about ourselves (substantive moral agreements we make, etc.) that are themselves predicated on our having a kind of psychology that we’re not yet ready to attribute to even our most impressive AI. But S&K’s argument is compatible with all of this — viz., with granting that obligations oftentimes for creatures like us arise out of features AI lack. What mat- ters for their argument is just that AI are candidates for e-function–generated obliga- tions, and it looks like this is something we can deny only on pain of denying either that AI can have e-functions, or that e-functions can generate norms. I think we should simply grant both of these — rather than incur what looks like an explana- tory burden to deny either. A fuller exposition of the details of these ideas are spelled out in S&K work on trustworthiness more generally, i.e., in Kelp and Simion (2023). For some representative defences of this idea, see, e.g., Graham (2014), Simion (2019), Kelp (2018), Kelp and Simion (2021). 1 3 Asian Journal of Philosophy (2023) 2:18 Page 5 of 8 18 The right place to press them, I think, is on the scope of the generalisability of their account. Here it will be helpful to consider again the case of a cancer-diagnos- tic AI which they use for illustrative purposes. The etiological function that such cancer diagnostic AIs acquire (which aligns with their d-function) is going to be a purely representational function. Cancer diagnostic algorithms are updated during the AI’s supervised learning process (i.e., as is standard in deep learning) against the metric of representational accuracy; the aim here is reliably accurately identifying (and not misidentifying) e.g., tumours from images, and thus to maximise represen- tational accuracy via sensitivity and specificity in its classifications. The AI becomes valuable to the designer when and only when, and to the extent that, this is achieved. To use but one example, take the case of bladder cancer diagnosis. It is diffi- cult using standard human tools to reliably predict the metastatic potential of dis- ease from the appearance of tumours. Digital pathology via deep learning AI is now more reliable than humans at this task, and so can predict disease with greater accu- racy than through use of human tools alone (see Harmon et al., 2020). This predic- tive accuracy explains the continued use (and further accuracy-aimed calibration by the designers) of such diagnostic AIs. There are other non-diagnostic AIs with representational functions as their e-functions. An example is FaceNet, which is optimised for accuracy in identifying faces from images (Schroff et al., 2015; William et al., 2019). AIs with purely representational e-functions, however, are — perhaps not sur- prisingly — an outlier in AI more broadly. Let’s begin here by considering just a few examples of the latest deep learning AI from Google’s DeepMind. AlphaCode, for instance, is optimised not for representational accuracy but for practically use- ful coding. Supervised training, in this case, is not done against a representational (mind-to-world) metric, but against a kind of usefulness (world-to-mind) metric. In competitive coding competitions, for instance, AlphaCode’s success (and what explains its continued existence) is developing coding solutions to practical coding problems and puzzles. Perhaps even more ambitiously, the research team at DeepMind is developing an AI optimised to ‘interact’ in human-like ways three-dimensional space in a simu- lated 3-D world (Abramson et  al. 2022). This AI is optimised in such a way that it will (given this aim) acquire an e-function that is at most only partly representa- tional (e.g., reliably identifying certain kinds of behaviour cues), while also partly practical (moving objects in the 3-D world). Next, and perhaps most notably, consider — in this case due to the OpenAI research team — ChatGPT, a chatbot built on OpenAI’s GPT-3 language models, and which provides ‘human-like’ responses to a wide range of queries. Although ChatGPT is often used for purposes of ‘fact finding’ (e.g., you can ask ChatGPT to explain complex phenomena to you), it is not right to say that this AI has a rep- resentational e-function. On the contrary, ChatGPT is optimised for conversational fluency; to the extent that accuracy misaligns with conversational fluency, ChatGPT is optimised to favour the fluency metric. See, e.g., https:// www. youtu be. com/ playl ist? list= PLJ1s thn_ UneUQ 2avq5 yCVsz cbmcm bege6 1 3 18 Page 6 of 8 Asian Journal of Philosophy (2023) 2:18 Finally, consider a familiar AI — YouTube’s recommender system — which is optimised against the metric of (in short) ‘keeping people watching’, and thus, gen- erating advertising revenue (Alfano et  al., 2020). When the accuracy of a recom- mendation choice (with respect to clustering towards videos of a similar content- type which the user has watched) misaligns with a choice more likely to keep the user watching more content, the algorithm is optimised to recommend the latter. This feature of YouTube’s recommender system has been identified as playing a role in the disproportional recommendation of conspiratorial content on YouTube rela- tive to viewers ex ante search queries. With the above short survey in mind, let’s now return to the matter of the scope of the generalisability of their S&K’s account of trustworthy AI. As I see it, at least, S&K’s account can explain trustworthy AI in cases where AI acquires representa- tional e-functions, such as the diagnostic AI example, and other AIs with represen- tational functions, like FaceNet. But — and here is where I am less confident about their account — we’ve just seen that many of the most touted and promising recent AIs either lack a representational e-functions altogether (e.g., AlphaCode, ChatGPT, etc.) or have such a function but only alongside other practical e-functions (e.g., DeepMind’s virtual world AI). S&K seem to face a dilemma here. On the one hand, if e-function generated obli- gations of the sort that a disposition to fulfil them matters for AI trustworthiness are not limited to those obligations generated by representational e-functions (but also include obligations generated by non-representational e-functions), then it looks like the view — problematically — predicts that YouTube’s recommender system, a known source of conspiratorial content, is maximally trustworthy so long as it is maximally fulfilling all the obligations generated by the e-function it has to ‘keep viewers watching’ (in turn, maximising ad revenue profits). I take it that this result is a non-starter; in so far as S&K are aiming to distinguish trustworthy from untrust- worthy AIs, YouTube’s recommender system has features that will line up as a para- digmatic case of the latter. Which brings us to the more plausible option and restrictive option: which is for a proponent of S&K’s view of trustworthy AI to hold that e-function–generated obli- gations of the sort that a disposition to fulfil them matters for AI trustworthiness are limited to those obligations generated by representational e-functions — such as, e.g., cancer diagnostic AIs, FaceNet, etc. Let’s assume this latter more restrictive route is taken. On this assumption, we seem to get the result that, on S&K’s view, all but the minority of AIs being devel- oped (those like cancer diagnostic AIs, FaceNet, etc.) fail to meet the conditions for trustworthy AI. So does this result overpredict untrustworthiness in AI? Here is one reason for thinking that perhaps it does. Even if we grant that, e.g., YouTube’s recommender See Alfano et al. (2020). Along with research by Alfano et al. (2020), see also an analysis of YouTube’s recommender system by Dündar and Ranaivoson (2022), which shows how the system is untrustworthy in the case of climate science information by generating misleading filter bubbles that suppress evidence. 1 3 Asian Journal of Philosophy (2023) 2:18 Page 7 of 8 18 system (in virtue of its documented propensity to recommend conspiratorial con- tent, a propensity that aligns with its fulfilling its practical e-function) is an exam- ple of an ‘untrustworthy AI’ (and agree that S&K’s view predicts untrustworthi- ness correctly here), it’s less clear that, e.g., AlphaCode should get classed together with YouTube’s recommender system. At least, it’s not clear to me what resources S&K’s proposal have for distinguishing them given that neither has been optimised to acquire a representational e-function. Without some additional story here, then, the concern is that S&K might overpredict untrustworthy AI even granting that the view diagnoses some cases of untrustworthy AI (e.g., YouTube’s recommender sys- tem) as it should. 4 Concluding remarks Giving a plausible account of trustworthy AI is no easy task; it is no surprise that, at least in 2023, the themes of trustworthy and responsible AI are among the most widely funded S&K’s account offers a welcome intervention in this debate because it clarifies the kind of anthropocentric barrier to getting a plausible account up and running from the very beginning, and it offers an example of how such an account that avoids this problem might go. My quibbles with the scope of the account in Sect.  3 remain, but they should be understood as just that: quibbles that invite fur- ther development of an account that is, on the whole, a promising one. Funding For supporting this research I am grateful  to the AHRC Digital Knowledge (AH/W008424/1) project as well as to the European Research Council (ERC)  under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948356,  KnowledgeLab: Knowledge-First Social Epistemology). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Abramson, J., Ahuja, A., Carnevale, F., Georgiev, P., Goldin, A., Hung, A., Landon, J., et  al. (2022). Improving multimodal interactive agents with reinforcement learning from human feedback. arXiv. https:// doi. org/ 10. 48550/ arXiv. 2211. 11602 Along with being a regular topic funded by academic funding agencies, the importance of the ques- tion is also reflected in government-level funding. See, e.g., this initiative by the UK government https:// apply- for- innov ation- fundi ng. ser vi ce. gov . uk/ com pe tition/ 1408/ over v iew/ e8b03 fe9- ca18- 415a- 852d- 343f4 231c4 42 Thanks to a reviewer at the Asian Journal of Philosophy and to  the AHRC Digital Knowl- edge research team for helpful feedback and discussion. 1 3 18 Page 8 of 8 Asian Journal of Philosophy (2023) 2:18 Adam Carter, J. (2022). Trust and trustworthiness. Philosophy and Phenomenological Research. Adam Carter, J., & Simion, M. (2020). The ethics and epistemology of trust. Internet Encyclopedia of Philosophy. Alfano, M., Fard, E. A., Carter, J. A., Clutton, P., & Klein C. (2020). Technologically scaffolded atypical cognition: The case of YouTube’s recommender system. Synthese, 1–24. Dündar, P., & Ranaivoson, H. (2022). Science by YouTube: ananalysis of YouTube’s recommendations on the climate change issue. Observatorio (OBS*), 16(3). Frost-Arnold, K. (2014). Trustworthiness and truth: The epistemic pitfalls of internet accountability. Episteme, 11(1), 63–81. https:// doi. org/ 10. 1017/ epi. 2013. 43 Graham, P. J. (2014). Functions, warrant, history. In Fairweather, A., & Flanagan, O. (Eds), Naturalizing Epistemic Virtue (pp. 15–35). Cambridge University Press. Hardin, R. (1996). Trustworthiness. Ethics, 107(1), 26–42. Harmon, S. A., Sanford, T. H., Brown, G. T., Yang, C., Mehralivand, S., Jacob, J. M., Valera, V. A., et al. (2020). Multiresolution application of artificial intelligence in digital pathology for prediction of positive lymph nodes from primary tumors in bladder cancer. JCO Clinical Cancer Informatics, 4, 367–382. https:// doi. org/ 10. 1200/ CCI. 19. 00155 Hawley, K. (2019). How to be trustworthy. Oxford University Press. Jones, K. (2012). Trustworthiness. Ethics, 123(1), 61–85. https:// doi. org/ 10. 1086/ 667838 Kaur, D., Uslu, S., & Durresi, A. (2020). Requirements for trustworthy artificial intelligence–A review. In International Conference on Network-Based Information Systems (pp. 105–115). Springer. Kaur, D., Suleyman, U., Kaley, J. R., & Arjan, D. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 39–1 39:38. Kelp, C. (2018). Assertion: A function first account. Noûs, 52(2), 411–442. Kelp, C. (2021). and Mona Simion. A functionalist account of assertion. Cambridge University Press. Kelp, C., & Simion, M. (2023). What Is Trustworthiness? Nous, 00, 1–17. https:// doi. org/ 10. 1111/ nous. O’Neill, O. (2018). Linking trust to trustworthiness. International Journal of Philosophical Studies, 26(2), 293–300. https:// doi. org/ 10. 1080/ 09672 559. 2018. 14546 37 Schroff, F., Kalenichenko, D., & Philbin. J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (pp. 815–823). Simion, M. (2019). Knowledge-first functionalism. Philosophical Issues, 29(1), 254–267. Simion, M., & Kelp, C. (2023). Trustworthy artificial intelligence. Asian Journal of Philosophy, no. Spe- cial Inaugural Issue. William, I., Rachmawanto, E. H., Santoso H. A., & Sari S. A. (2019). Face recognition using Facenet (Survey, Performance Test, and Comparison). In 2019 Fourth International Conference on Infor- matics and Computing (ICIC) (pp. 1–6). IEEE. 1 3 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Philosophy Springer Journals

Simion and Kelp on trustworthy AI

Carter, J. Adam
Asian Journal of Philosophy , Volume 2 (1) – May 2, 2023
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Abstract

Simion and Kelp offer a prima facie very promising account of trustworthy AI. One benefit of the account is that it elegantly explains trustworthiness in the case of can- cer diagnostic AIs, which involve the acquisition by the AI of a representational eti- ological function. In this brief note, I offer some reasons to think that their account cannot be extended — at least not straightforwardly — beyond such cases (i.e., to cases of AIs with non-representational etiological functions) without incurring the unwanted cost of overpredicting untrustworthiness. Keywords Trustworthy AI · Trustworthiness · Etiological functionalism · Artificial intelligence 1 Introduction Increasingly, the question of whether — and if so under what conditions — artificial intelligence (AI) can be ‘trustworthy’ (as opposed to merely reliable or unreliable) is being debated by researchers across various disciplines with a stake in the matter, from computer science and medicine to psychology and politics. Given that the nature and norms of trustworthiness itself have been of longstand- ing interest in philosophy, philosophers of trust are well situated to help make pro- gress on this question. In their paper “Trustworthy Artificial Intelligence” (2023), Simion and Kelp (hereafter, S&K) aim to do just this. I think they largely succeed. That said, in this short note I am going to quibble with a few details. In short, I worry that their reliance on function-generated obli- gations in their account of trustworthy AI helps their proposal get exactly the right For some recent reviews, see, e.g., Kaur et al. (2022); and Kaur et al. (2020). See, e.g., Hardin (1996); Jones (2012); Frost-Arnold (2014); O’Neill (2018); Simion and Kelp (2023); Carter (2022). See also Carter and Simion (2020) for a review. * J. Adam Carter adam.carter@glasgow.ac.uk Reader in Philosophy, University of Glasgow, 67-69 Oakfield Avenue, Glasgow, Scotland, UK Vol.:(0123456789) 1 3 18 Page 2 of 8 Asian Journal of Philosophy (2023) 2:18 result in certain central AI cases, such as cancer diagnostic AIs, but at the potential cost of overpredicting untrustworthiness across a range of other AIs. Here’s the plan for the paper. In Sect.  2 I’ll provide a brief overview of S&K’s account of trustworthy AI, emphasising the core desiderata they take themselves to have met. In Sect. 3, I’ll then raise some potential worries, and discuss and critique some lines of reply. 2 S&K’s line of argument A natural strategy for giving an account of trustworthy AI will be a kind of ‘applica- tion’ strategy: (i) give a compelling account of trustworthiness simpliciter and then (ii) apply it to AI, and make explicit what follows, illuminating trustworthy AI in the process. But, as S&K note, there is a problem that faces many extant accounts of trustwor- thiness that might try to opt for that strategy. The problem is this: many accounts of trustworthiness are such that the psychological assumptions underlying them (e.g., that being trustworthy involves something like a good will or virtue) are simply too anthropocentric. As S&K ask: Do Als have something that is recognizable as goodwill? Can AIs host charac- ter virtues? Or, to put it more precisely, is it correct to think that AI capacity for trustworthiness co-varies with their capacity for hosting a will or character virtues? (p. 4). The situation seems to be this: an account of trustworthiness with strongly anthro- pocentric psychological features ‘baked in’ will either not be generalisable to AI (if AI lacks good will, virtue, etc.), or it will be generalisable only by those willing to embrace further strong positions about AI. Ceteris paribus, a more ‘generalisable’ account of trustworthiness, when it comes to an application to AI specifically, will be a less anthropocentric one that could sidestep the above problem. One candidate such account they identify is Hawley’s (2019) negative account of trustworthiness, on which trustworthiness is a matter of avoiding unfulfilled commitments. It is worth registering that some philosophers of trust might begin with the premise that trust and trust- worthiness are essentially anthropocentric; for instance, on the supposition that trust requires vulnerability to betrayal, one might think that one simply can’t be betrayed by artifacts or that which lacks agency. On this way of approaching the topic, Simion and Kelp are engaged with here, we might simply deflate talk of trustworthy AI to ‘reliable’ AI, and focus on conditions of rational reliance on AI. For my purposes here, however, I am assuming along with S&K, and also following an established research area on trustworthy autonomous systems, that in-principle extendability to AI things is desirable feature of a view of a philo- sophical view of trustworthiness. At any rate, in order to explore the interesting features of S&K’s article, I am not challenging the assumption they make that such extendability is not misguided. Another recent account of trustworthiness that is arguably not too anthropocentric that it would be dif- ficult to generalise over to AI is the performance-theoretic account of trustworthiness. See here, Carter (2022). 1 3 Asian Journal of Philosophy (2023) 2:18 Page 3 of 8 18 S&K have argued elsewhere at length for a different — and similarly non overtly anthropocentric — account of trustworthiness, which they take to have advantages (I won’t summarise these here) over Hawley’s: on S&K’s preferred account, trustwor- thiness is understood as a disposition to fulfil one’s obligations. What is prima facie attractive about an obligation-centric account of trustworthi- ness, for the purpose of generalising that account to trustworthy AI, is that (i) arti- facts can have functions; and (ii) functions can generate obligations. Let’s look at the first point first. S&K distinguish between design functions (d-functions), sourced in the designer’s intentions, and etiological functions (e-func- tions), sourced in a history of success, noting that artefacts can acquire both kinds of functions. S&K use the example of a knife to capture this point: My knife, for instance, has the design function to cut because that was, plausi- bly, the intention of its designer. At the same time, my knife also has an etio- logical function to cut: that is because tokens of its type have cut in the past, which was beneficial to my ancestors, and which contributes to the explana- tion of the continuous existence of knives. When artefacts acquire etiologi- cal functions on top of their design functions, they thereby acquire a new set of norms governing their functioning, sourced in their etiological functions. Design-wise, my knife is properly functioning (henceforth properly d-func- tioning) insofar as it’s working in the way in which its designer intended it to work. Etiologically, my knife is properly functioning (henceforth properly e-functioning) insofar as it works in a way that reliably leads to cutting in nor- mal conditions (p. 9). While d-functions and e-functions (i.e., proper functioning) will often line up, these functions can come apart (e.g., when artifacts are designed to work in non- e-function-filling ways). When they don’t line up, S&K maintain that e-functions generally override. As they put it: what we usually see in cases of divergence is that norms governing proper- functioning tend to be incorporated in design plans of future generations of tokens of the type: if we discover that there are more reliable ways for the arte- fact in question to fulfil its function, design will follow suit (Ibid., p. 9). So we have in view now S&K’s thinking behind the idea that artifacts (of which AI is an instance) can acquire functions. What about the next component of the view: that functions can generate obligations? The crux of the idea is that a species of obligation, function-generated obligation, is implicated by facts about what it is for something to fulfil its e-function. The heart has a purely e-function–generated obligation to pump blood in normal conditions (the conditions under which pumping blood contributed to explanation of its contin- ued existence). In maintaining this, on S&K’s line, we aren’t doing anything objec- tionably anthropocentric, any more than when we say a heart should (qua heart) See Kelp and Simion (2023). 1 3 18 Page 4 of 8 Asian Journal of Philosophy (2023) 2:18 pump blood. We can easily extend this kind of obligation talk over to artifacts, then: just as a heart is malfunctioning (and so not meeting its e-functionally sourced obli- gations) if it stops pumping blood, a diagnostic AI is malfunctioning (and not meet- ing its e-functionally sourced obligations) if it stops recognising simple tumours by their appearance, and miscategorises them. Against this background, then, S&K define an AI’s being maximally trustwor - thy at phi-ing as being a matter of having a “maximally strong disposition to meet its functional norms-sourced obligations to phi.” The conditions for outright AI trustworthiness attributions can then be characterised in terms of maximal AI trust- worthiness in the following way: an outright attribution of trustworthiness to an AI is true in a context c iff that AI approximates “maximal trustworthiness to phi” closely enough to surpass a threshold on degrees of trustworthiness determined by c, where the closer x approximates maximal trustworthiness to phi, the higher x’s degree of trustworthiness to phi. 3 Critical discussion I suspect that a typical place one might begin to poke to look for a hole in the above account would be the very idea that a machine could have an obligation in the first place. Imagine this line or reply: “But S&K have complained that extant accounts of trustworthiness that rely on ‘virtue’ and ‘good will’ as psychologically demand- ing prerequisites for being trustworthy are too anthropocentric to be generalisable to AI. But isn’t being a candidate for an ‘obligation’ equally psychologically demand- ing and thereby anthropocentric? If so, haven’t they failed their own generalisability desiderata by their own lights?”. The above might look superficially like the right way to press S&K, but I think such a line would be uncharitable, so much so that it’s not worth pursuing. First, we humans often have our own obligations to others sourced in facts about ourselves (substantive moral agreements we make, etc.) that are themselves predicated on our having a kind of psychology that we’re not yet ready to attribute to even our most impressive AI. But S&K’s argument is compatible with all of this — viz., with granting that obligations oftentimes for creatures like us arise out of features AI lack. What mat- ters for their argument is just that AI are candidates for e-function–generated obliga- tions, and it looks like this is something we can deny only on pain of denying either that AI can have e-functions, or that e-functions can generate norms. I think we should simply grant both of these — rather than incur what looks like an explana- tory burden to deny either. A fuller exposition of the details of these ideas are spelled out in S&K work on trustworthiness more generally, i.e., in Kelp and Simion (2023). For some representative defences of this idea, see, e.g., Graham (2014), Simion (2019), Kelp (2018), Kelp and Simion (2021). 1 3 Asian Journal of Philosophy (2023) 2:18 Page 5 of 8 18 The right place to press them, I think, is on the scope of the generalisability of their account. Here it will be helpful to consider again the case of a cancer-diagnos- tic AI which they use for illustrative purposes. The etiological function that such cancer diagnostic AIs acquire (which aligns with their d-function) is going to be a purely representational function. Cancer diagnostic algorithms are updated during the AI’s supervised learning process (i.e., as is standard in deep learning) against the metric of representational accuracy; the aim here is reliably accurately identifying (and not misidentifying) e.g., tumours from images, and thus to maximise represen- tational accuracy via sensitivity and specificity in its classifications. The AI becomes valuable to the designer when and only when, and to the extent that, this is achieved. To use but one example, take the case of bladder cancer diagnosis. It is diffi- cult using standard human tools to reliably predict the metastatic potential of dis- ease from the appearance of tumours. Digital pathology via deep learning AI is now more reliable than humans at this task, and so can predict disease with greater accu- racy than through use of human tools alone (see Harmon et al., 2020). This predic- tive accuracy explains the continued use (and further accuracy-aimed calibration by the designers) of such diagnostic AIs. There are other non-diagnostic AIs with representational functions as their e-functions. An example is FaceNet, which is optimised for accuracy in identifying faces from images (Schroff et al., 2015; William et al., 2019). AIs with purely representational e-functions, however, are — perhaps not sur- prisingly — an outlier in AI more broadly. Let’s begin here by considering just a few examples of the latest deep learning AI from Google’s DeepMind. AlphaCode, for instance, is optimised not for representational accuracy but for practically use- ful coding. Supervised training, in this case, is not done against a representational (mind-to-world) metric, but against a kind of usefulness (world-to-mind) metric. In competitive coding competitions, for instance, AlphaCode’s success (and what explains its continued existence) is developing coding solutions to practical coding problems and puzzles. Perhaps even more ambitiously, the research team at DeepMind is developing an AI optimised to ‘interact’ in human-like ways three-dimensional space in a simu- lated 3-D world (Abramson et  al. 2022). This AI is optimised in such a way that it will (given this aim) acquire an e-function that is at most only partly representa- tional (e.g., reliably identifying certain kinds of behaviour cues), while also partly practical (moving objects in the 3-D world). Next, and perhaps most notably, consider — in this case due to the OpenAI research team — ChatGPT, a chatbot built on OpenAI’s GPT-3 language models, and which provides ‘human-like’ responses to a wide range of queries. Although ChatGPT is often used for purposes of ‘fact finding’ (e.g., you can ask ChatGPT to explain complex phenomena to you), it is not right to say that this AI has a rep- resentational e-function. On the contrary, ChatGPT is optimised for conversational fluency; to the extent that accuracy misaligns with conversational fluency, ChatGPT is optimised to favour the fluency metric. See, e.g., https:// www. youtu be. com/ playl ist? list= PLJ1s thn_ UneUQ 2avq5 yCVsz cbmcm bege6 1 3 18 Page 6 of 8 Asian Journal of Philosophy (2023) 2:18 Finally, consider a familiar AI — YouTube’s recommender system — which is optimised against the metric of (in short) ‘keeping people watching’, and thus, gen- erating advertising revenue (Alfano et  al., 2020). When the accuracy of a recom- mendation choice (with respect to clustering towards videos of a similar content- type which the user has watched) misaligns with a choice more likely to keep the user watching more content, the algorithm is optimised to recommend the latter. This feature of YouTube’s recommender system has been identified as playing a role in the disproportional recommendation of conspiratorial content on YouTube rela- tive to viewers ex ante search queries. With the above short survey in mind, let’s now return to the matter of the scope of the generalisability of their S&K’s account of trustworthy AI. As I see it, at least, S&K’s account can explain trustworthy AI in cases where AI acquires representa- tional e-functions, such as the diagnostic AI example, and other AIs with represen- tational functions, like FaceNet. But — and here is where I am less confident about their account — we’ve just seen that many of the most touted and promising recent AIs either lack a representational e-functions altogether (e.g., AlphaCode, ChatGPT, etc.) or have such a function but only alongside other practical e-functions (e.g., DeepMind’s virtual world AI). S&K seem to face a dilemma here. On the one hand, if e-function generated obli- gations of the sort that a disposition to fulfil them matters for AI trustworthiness are not limited to those obligations generated by representational e-functions (but also include obligations generated by non-representational e-functions), then it looks like the view — problematically — predicts that YouTube’s recommender system, a known source of conspiratorial content, is maximally trustworthy so long as it is maximally fulfilling all the obligations generated by the e-function it has to ‘keep viewers watching’ (in turn, maximising ad revenue profits). I take it that this result is a non-starter; in so far as S&K are aiming to distinguish trustworthy from untrust- worthy AIs, YouTube’s recommender system has features that will line up as a para- digmatic case of the latter. Which brings us to the more plausible option and restrictive option: which is for a proponent of S&K’s view of trustworthy AI to hold that e-function–generated obli- gations of the sort that a disposition to fulfil them matters for AI trustworthiness are limited to those obligations generated by representational e-functions — such as, e.g., cancer diagnostic AIs, FaceNet, etc. Let’s assume this latter more restrictive route is taken. On this assumption, we seem to get the result that, on S&K’s view, all but the minority of AIs being devel- oped (those like cancer diagnostic AIs, FaceNet, etc.) fail to meet the conditions for trustworthy AI. So does this result overpredict untrustworthiness in AI? Here is one reason for thinking that perhaps it does. Even if we grant that, e.g., YouTube’s recommender See Alfano et al. (2020). Along with research by Alfano et al. (2020), see also an analysis of YouTube’s recommender system by Dündar and Ranaivoson (2022), which shows how the system is untrustworthy in the case of climate science information by generating misleading filter bubbles that suppress evidence. 1 3 Asian Journal of Philosophy (2023) 2:18 Page 7 of 8 18 system (in virtue of its documented propensity to recommend conspiratorial con- tent, a propensity that aligns with its fulfilling its practical e-function) is an exam- ple of an ‘untrustworthy AI’ (and agree that S&K’s view predicts untrustworthi- ness correctly here), it’s less clear that, e.g., AlphaCode should get classed together with YouTube’s recommender system. At least, it’s not clear to me what resources S&K’s proposal have for distinguishing them given that neither has been optimised to acquire a representational e-function. Without some additional story here, then, the concern is that S&K might overpredict untrustworthy AI even granting that the view diagnoses some cases of untrustworthy AI (e.g., YouTube’s recommender sys- tem) as it should. 4 Concluding remarks Giving a plausible account of trustworthy AI is no easy task; it is no surprise that, at least in 2023, the themes of trustworthy and responsible AI are among the most widely funded S&K’s account offers a welcome intervention in this debate because it clarifies the kind of anthropocentric barrier to getting a plausible account up and running from the very beginning, and it offers an example of how such an account that avoids this problem might go. My quibbles with the scope of the account in Sect.  3 remain, but they should be understood as just that: quibbles that invite fur- ther development of an account that is, on the whole, a promising one. Funding For supporting this research I am grateful  to the AHRC Digital Knowledge (AH/W008424/1) project as well as to the European Research Council (ERC)  under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948356,  KnowledgeLab: Knowledge-First Social Epistemology). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Abramson, J., Ahuja, A., Carnevale, F., Georgiev, P., Goldin, A., Hung, A., Landon, J., et  al. (2022). Improving multimodal interactive agents with reinforcement learning from human feedback. arXiv. https:// doi. org/ 10. 48550/ arXiv. 2211. 11602 Along with being a regular topic funded by academic funding agencies, the importance of the ques- tion is also reflected in government-level funding. 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Journal

Asian Journal of Philosophy – Springer Journals

Published: May 2, 2023

Keywords: Trustworthy AI; Trustworthiness; Etiological functionalism; Artificial intelligence

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