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[[Marvin Minsky]] et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI.<ref>Minsky, et al., "The Society of Intelligent Veillance" IEEE ISTAS2013, pages 13-17.</ref>
Modern complex AI techniques, such as [[deep learning]], are naturally opaque.<ref>{{cite magazine|last1=Mukherjee|first1=Siddhartha|title=A.I. Versus M.D.|url=https://www.newyorker.com/magazine/2017/04/03/ai-versus-md|access-date=30 January 2018|magazine=The New Yorker|date=27 March 2017}}</ref> To address this issue, methods have been developed to make new models more explainable and interpretable.<ref>{{Cite journal|date=2020-07-08|title=Interpretable neural networks based on continuous-valued logic and multicriteria decision operators|journal=Knowledge-Based Systems|language=en|volume=199|pages=105972|doi=10.1016/j.knosys.2020.105972 |arxiv=1910.02486 |issn=0950-7051|doi-access=free|last1=Csiszár|first1=Orsolya|last2=Csiszár|first2=Gábor|last3=Dombi|first3=József}}</ref><ref name="Lipton 31–57"/><ref name="Interpretable machine learning: def"/><ref>{{cite arXiv|last1=Doshi-Velez|first1=Finale|last2=Kim|first2=Been|date=2017-02-27|title=Towards A Rigorous Science of Interpretable Machine Learning|eprint=1702.08608|class=stat.ML}}</ref><ref>{{Cite arXiv |last=Abdollahi, Behnoush, and Olfa Nasraoui.|title=Explainable Restricted Boltzmann Machines for Collaborative Filtering.|eprint=1606.07129|class=stat.ML|year=2016}}</ref><ref>{{Cite book|last1=Dombi|first1=József|last2=Csiszár|first2=Orsolya|series=Studies in Fuzziness and Soft Computing |date=2021|title=Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools|url=https://link.springer.com/book/10.1007/978-3-030-72280-7|volume=408|language=en-gb|doi=10.1007/978-3-030-72280-7|isbn=978-3-030-72279-1|s2cid=233486978|issn=1434-9922}}</ref> This includes layerwise relevance propagation (LRP), a technique for determining which features in a particular input vector contribute most strongly to a neural network's output.<ref name="Bach Binder Montavon Klauschen p=e0130140">{{cite journal|last1=Bach|first1=Sebastian|last2=Binder|first2=Alexander|last3=Montavon|first3=Grégoire|last4=Klauschen|first4=Frederick|last5=Müller|first5=Klaus-Robert|author-link5=Klaus-Robert Müller|last6=Samek|first6=Wojciech|date=2015-07-10|editor-last=Suarez|editor-first=Oscar Deniz|title=On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation|journal=PLOS ONE|volume=10|issue=7|page=e0130140|bibcode=2015PLoSO..1030140B|doi=10.1371/journal.pone.0130140|issn=1932-6203|pmc=4498753|pmid=26161953|doi-access=free}}</ref><ref>{{cite news|url=https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial|title=Computer says no: why making AIs fair, accountable and transparent is crucial|last1=Sample|first1=Ian|date=5 November 2017|work=The Guardian|access-date=5 August 2018|language=en}}</ref> Other techniques explain some particular prediction made by a (nonlinear) black-box model, a goal referred to as "local interpretability".<ref>{{Cite journal|last1=Martens|first1=David|last2=Provost|first2=Foster|title=Explaining data-driven document classifications|url=http://pages.stern.nyu.edu/~fprovost/Papers/MartensProvost_Explaining.pdf|journal=MIS Quarterly|year=2014|volume=38|pages=73–99|doi=10.25300/MISQ/2014/38.1.04|s2cid=14238842}}</ref><ref>{{Cite journal|title="Why Should I Trust You?" {{!}} Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|language=EN|doi=10.1145/2939672.2939778|s2cid=13029170}}</ref><ref>{{Citation|last1=Lundberg|first1=Scott M|title=A Unified Approach to Interpreting Model Predictions|date=2017|url=http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf|work=Advances in Neural Information Processing Systems 30|pages=4765–4774|editor-last=Guyon|editor-first=I.|publisher=Curran Associates, Inc.|access-date=2020-03-13|last2=Lee|first2=Su-In|editor2-last=Luxburg|editor2-first=U. V.|editor3-last=Bengio|editor3-first=S.|editor4-last=Wallach|editor4-first=H.|arxiv=1705.07874}}</ref><ref>{{Cite journal|last1=Carter|first1=Brandon|last2=Mueller|first2=Jonas|last3=Jain|first3=Siddhartha|last4=Gifford|first4=David|date=2019-04-11|title=What made you do this? Understanding black-box decisions with sufficient input subsets|url=http://proceedings.mlr.press/v89/carter19a.html|journal=The 22nd International Conference on Artificial Intelligence and Statistics|language=en|pages=567–576}}</ref><ref>{{Cite journal|last1=Shrikumar|first1=Avanti|last2=Greenside|first2=Peyton|last3=Kundaje|first3=Anshul|date=2017-07-17|title=Learning Important Features Through Propagating Activation Differences|url=http://proceedings.mlr.press/v70/shrikumar17a.html|journal=International Conference on Machine Learning|language=en|pages=3145–3153}}</ref><ref>{{Cite journal|url=https://dl.acm.org/doi/abs/10.5555/3305890.3306024|title=Axiomatic attribution for deep networks {{!}} Proceedings of the 34th International Conference on Machine Learning - Volume 70|website=dl.acm.org|series=Icml'17|date=6 August 2017|pages=3319–3328|language=EN|access-date=2020-03-13}}</ref>
There has been work on making glass-box models which are more transparent to inspection.<ref name=":6"/><ref>{{cite journal |last1=Singh |first1=Chandan |last2=Nasseri |first2=Keyan |last3=Tan |first3=Yan Shuo |last4=Tang |first4=Tiffany |last5=Yu |first5=Bin |title=imodels: a python package for fitting interpretable models |journal=Journal of Open Source Software |date=4 May 2021 |volume=6 |issue=61 |pages=3192 |doi=10.21105/joss.03192 |bibcode=2021JOSS....6.3192S |s2cid=235529515 |url=https://joss.theoj.org/papers/10.21105/joss.03192 |language=en |issn=2475-9066}}</ref> This includes [[decision tree]]s,<ref>{{Cite journal|last1=Vidal|first1=Thibaut|last2=Schiffer|first2=Maximilian|date=2020|title=Born-Again Tree Ensembles|url=http://proceedings.mlr.press/v119/vidal20a.html|journal=International Conference on Machine Learning|language=en|publisher=PMLR|volume=119|pages=9743–9753|arxiv=2003.11132}}</ref> [[Bayesian network]]s, sparse [[linear model]]s,<ref>{{cite journal |last1=Ustun |first1=Berk |last2=Rudin |first2=Cynthia |title=Supersparse linear integer models for optimized medical scoring systems |journal=Machine Learning |date=1 March 2016 |volume=102 |issue=3 |pages=349–391 |doi=10.1007/s10994-015-5528-6 |s2cid=207211836 |url=https://link.springer.com/article/10.1007/s10994-015-5528-6 |language=en |issn=1573-0565}}</ref> and more.<ref>Bostrom, N., & Yudkowsky, E. (2014). [https://intelligence.org/files/EthicsofAI.pdf The ethics of artificial intelligence]. ''The Cambridge Handbook of Artificial Intelligence'', 316-334.</ref> The [[ACM Conference on Fairness, Accountability, and Transparency|Association for Computing Machinery Conference on Fairness, Accountability, and Transparency (ACM FAccT)]] was established in 2018 to study transparency and explainability in the context of socio-technical systems, many of which include artificial intelligence.<ref name="FAT* conference">{{cite web | url=https://fatconference.org/ | title=FAT* Conference }}</ref><ref>{{cite news |title=Computer programs recognise white men better than black women |url=https://www.economist.com/science-and-technology/2018/02/15/computer-programs-recognise-white-men-better-than-black-women |access-date=5 August 2018 |newspaper=The Economist |date=2018 |language=en}}</ref>
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