{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:07:32Z","timestamp":1776157652625,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":112,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,12]]},"DOI":"10.1145\/3593013.3594087","type":"proceedings-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T14:40:46Z","timestamp":1686580846000},"page":"1369-1385","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":140,"title":["Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-6136","authenticated-orcid":false,"given":"Vivian","family":"Lai","sequence":"first","affiliation":[{"name":"University of Colorado Boulder, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6101-2150","authenticated-orcid":false,"given":"Chacha","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Chicago, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6600-267X","authenticated-orcid":false,"given":"Alison","family":"Smith-Renner","sequence":"additional","affiliation":[{"name":"Dataminr Inc., USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4543-7196","authenticated-orcid":false,"given":"Q. Vera","family":"Liao","sequence":"additional","affiliation":[{"name":"Microsoft Research, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3981-2116","authenticated-orcid":false,"given":"Chenhao","family":"Tan","sequence":"additional","affiliation":[{"name":"University of Chicago, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1\u201314","author":"Abdul Ashraf","unstructured":"Ashraf Abdul, Christian von der Weth, Mohan Kankanhalli, and Brian Y Lim. 2020. COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1\u201314."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377519"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v35i4.2513"},{"key":"e_1_3_2_1_4_1","volume-title":"Machine bias. ProPublica. See https:\/\/www. propublica. org\/article\/machine-bias-risk-assessments-in-criminal-sentencing","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica. See https:\/\/www. propublica. org\/article\/machine-bias-risk-assessments-in-criminal-sentencing (2016)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445736"},{"key":"e_1_3_2_1_6_1","first-page":"799","article-title":"System and method for predicting consumer credit risk using income risk based credit score","volume":"8","author":"Annappindi Suresh Kumar","year":"2014","unstructured":"Suresh Kumar Annappindi. 2014. System and method for predicting consumer credit risk using income risk based credit score. US Patent 8,799,150.","journal-title":"US Patent"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942288"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2838739.2838753"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/hcomp.v7i1.5285"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012429"},{"key":"e_1_3_2_1_11_1","volume-title":"Marco Tulio Ribeiro, and Daniel S. Weld","author":"Bansal Gagan","year":"2020","unstructured":"Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S. Weld. 2020. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance. arXiv preprint arXiv:2006.14779 (2020)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/202"},{"key":"e_1_3_2_1_14_1","volume-title":"The values encoded in machine learning research. arXiv preprint arXiv:2106.15590","author":"Birhane Abeba","year":"2021","unstructured":"Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao. 2021. The values encoded in machine learning research. arXiv preprint arXiv:2106.15590 (2021)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377498"},{"key":"e_1_3_2_1_16_1","volume-title":"Maja Barbara Malaya, and Krzysztof Z Gajos","author":"Bu\u00e7inca Zana","year":"2021","unstructured":"Zana Bu\u00e7inca, Maja Barbara Malaya, and Krzysztof Z Gajos. 2021. To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making. arXiv preprint arXiv:2102.09692 (2021)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2015.26"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302289"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300234"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v14i1.7282"},{"key":"e_1_3_2_1_21_1","volume-title":"Do explanations make VQA models more predictable to a human?arXiv preprint arXiv:1810.12366","author":"Chandrasekaran Arjun","year":"2018","unstructured":"Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh. 2018. Do explanations make VQA models more predictable to a human?arXiv preprint arXiv:1810.12366 (2018)."},{"key":"e_1_3_2_1_22_1","volume-title":"Machine explanations and human understanding. arXiv preprint arXiv:2202.04092","author":"Chen Chacha","year":"2022","unstructured":"Chacha Chen, Shi Feng, Amit Sharma, and Chenhao Tan. 2022. Machine explanations and human understanding. arXiv preprint arXiv:2202.04092 (2022)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300789"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397481.3450644"},{"key":"e_1_3_2_1_25_1","volume-title":"Construct validity in psychological tests.Psychological bulletin 52, 4","author":"Cronbach Lee J","year":"1955","unstructured":"Lee J Cronbach and Paul E Meehl. 1955. Construct validity in psychological tests.Psychological bulletin 52, 4 (1955), 281."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377512"},{"key":"e_1_3_2_1_27_1","volume-title":"Amazon scraps secret AI recruiting tool that showed bias against women","author":"Dastin Jeffrey","year":"2016","unstructured":"Jeffrey Dastin. 2016. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. See https:\/\/www.reuters.com\/article\/us-amazon-com-jobs-automation-insight\/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G (2016)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376638"},{"key":"e_1_3_2_1_29_1","first-page":"114","article-title":"Algorithm aversion: People erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology","volume":"144","author":"Dietvorst Berkeley J","year":"2015","unstructured":"Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015. Algorithm aversion: People erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General 144, 1 (2015), 114.","journal-title":"General"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2016.2643"},{"key":"e_1_3_2_1_31_1","volume-title":"Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current cardiology reports 16, 1","author":"Dilsizian Steven E","year":"2014","unstructured":"Steven E Dilsizian and Eliot L Siegel. 2014. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current cardiology reports 16, 1 (2014), 441."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302310"},{"key":"e_1_3_2_1_33_1","volume-title":"The accuracy, fairness, and limits of predicting recidivism. Science advances 4, 1","author":"Dressel Julia","year":"2018","unstructured":"Julia Dressel and Hany Farid. 2018. The accuracy, fairness, and limits of predicting recidivism. Science advances 4, 1 (2018), eaao5580."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1943403.1943424"},{"key":"e_1_3_2_1_35_1","volume-title":"What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play. arXiv preprint arXiv:1810.09648","author":"Feng Shi","year":"2018","unstructured":"Shi Feng and Jordan Boyd-Graber. 2018. What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play. arXiv preprint arXiv:1810.09648 (2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"Carlos Scheidegger, and Dylan Slack.","author":"Friedler Sorelle A","year":"2019","unstructured":"Sorelle A Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, and Dylan Slack. 2019. Assessing the Local Interpretability of Machine Learning Models. arXiv preprint arXiv:1902.03501 (2019)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/332040.332455"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376316"},{"key":"e_1_3_2_1_39_1","volume-title":"Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience. arXiv preprint arXiv:2001.09219","author":"Ghai Bhavya","year":"2020","unstructured":"Bhavya Ghai, Q Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller. 2020. Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience. arXiv preprint arXiv:2001.09219 (2020)."},{"key":"e_1_3_2_1_40_1","volume-title":"Jiri Navratil, Prasanna Sattigeri, Kush R Varshney, and Yunfeng Zhang.","author":"Ghosh Soumya","year":"2021","unstructured":"Soumya Ghosh, Q Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R Varshney, and Yunfeng Zhang. 2021. Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI. arXiv preprint arXiv:2106.01410 (2021)."},{"key":"e_1_3_2_1_41_1","volume-title":"Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA. arXiv preprint arXiv:2012.15075","author":"Gonzalez Ana Valeria","year":"2020","unstructured":"Ana Valeria Gonzalez, Gagan Bansal, Angela Fan, Robin Jia, Yashar Mehdad, and Srinivasan Iyer. 2020. Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA. arXiv preprint arXiv:2012.15075 (2020)."},{"key":"e_1_3_2_1_42_1","volume-title":"The Flaws of Policies Requiring Human Oversight of Government Algorithms. Available at SSRN","author":"Green Ben","year":"2021","unstructured":"Ben Green. 2021. The Flaws of Policies Requiring Human Oversight of Government Algorithms. Available at SSRN (2021)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287563"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359152"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3465622"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300803"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372831"},{"key":"e_1_3_2_1_48_1","volume-title":"Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?arXiv preprint arXiv:2005.01831","author":"Hase Peter","year":"2020","unstructured":"Peter Hase and Mohit Bansal. 2020. Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?arXiv preprint arXiv:2005.01831 (2020)."},{"key":"e_1_3_2_1_49_1","volume-title":"Visual analytics in deep learning: An interrogative survey for the next frontiers","author":"Hohman Fred","year":"2018","unstructured":"Fred Hohman, Minsuk Kahng, Robert Pienta, and Duen Horng Chau. 2018. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE transactions on visualization and computer graphics 25, 8 (2018), 2674\u20132693."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445385"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445923"},{"key":"e_1_3_2_1_52_1","volume-title":"The limits of human predictions of recidivism. Science Advances 6, 7","author":"Jung Jongbin","year":"2020","unstructured":"Jongbin Jung, Sharad Goel, Jennifer Skeem, 2020. The limits of human predictions of recidivism. Science Advances 6, 7 (2020), eaaz0652."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858558"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbankfin.2010.06.001"},{"key":"e_1_3_2_1_55_1","volume-title":"Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ digital medicine 3, 1","author":"Kiani Amirhossein","year":"2020","unstructured":"Amirhossein Kiani, Bora Uyumazturk, Pranav Rajpurkar, Alex Wang, Rebecca Gao, Erik Jones, Yifan Yu, Curtis P Langlotz, Robyn L Ball, Thomas J Montine, 2020. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ digital medicine 3, 1 (2020), 1\u20138."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300641"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2207678"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/VLHCC.2013.6645235"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300717"},{"key":"e_1_3_2_1_60_1","volume-title":"An evaluation of the human-interpretability of explanation. arXiv preprint arXiv:1902.00006","author":"Lage Isaac","year":"2019","unstructured":"Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2019. An evaluation of the human-interpretability of explanation. arXiv preprint arXiv:1902.00006 (2019)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376873"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287590"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939874"},{"key":"e_1_3_2_1_64_1","volume-title":"A quantitative approach to content validity. Personnel psychology 28, 4","author":"Lawshe Charles H","year":"1975","unstructured":"Charles H Lawshe. 1975. A quantitative approach to content validity. Personnel psychology 28, 4 (1975), 563\u2013575."},{"key":"e_1_3_2_1_65_1","volume-title":"Trust in automation: Designing for appropriate reliance. Human factors 46, 1","author":"Lee John D","year":"2004","unstructured":"John D Lee and Katrina A See. 2004. Trust in automation: Designing for appropriate reliance. Human factors 46, 1 (2004), 50\u201380."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3415227"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445472"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359284"},{"key":"e_1_3_2_1_69_1","volume-title":"Explanation-Based Human Debugging of NLP Models: A Survey. arXiv preprint arXiv:2104.15135","author":"Lertvittayakumjorn Piyawat","year":"2021","unstructured":"Piyawat Lertvittayakumjorn and Francesca Toni. 2021. Explanation-Based Human Debugging of NLP Models: A Survey. arXiv preprint arXiv:2104.15135 (2021)."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445522"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/1518701.1519023"},{"key":"e_1_3_2_1_72_1","volume-title":"Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making. arXiv preprint arXiv:2101.05303","author":"Liu Han","year":"2021","unstructured":"Han Liu, Vivian Lai, and Chenhao Tan. 2021. Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making. arXiv preprint arXiv:2101.05303 (2021)."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.obhdp.2018.12.005"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445562"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372824"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376257"},{"key":"e_1_3_2_1_77_1","volume-title":"When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. arXiv preprint arXiv:2011.06167","author":"McGrath Sean","year":"2020","unstructured":"Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, and Himabindu Lakkaraju. 2020. When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. arXiv preprint arXiv:2011.06167 (2020)."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_1_79_1","volume-title":"How do humans understand explanations from machine learning systems? an evaluation of the human-interpretability of explanation. arXiv preprint arXiv:1802.00682","author":"Narayanan Menaka","year":"2018","unstructured":"Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2018. How do humans understand explanations from machine learning systems? an evaluation of the human-interpretability of explanation. arXiv preprint arXiv:1802.00682 (2018)."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1097"},{"key":"e_1_3_2_1_81_1","volume-title":"Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In 26th International Conference on Intelligent User Interfaces. 340\u2013350","author":"Nourani Mahsan","year":"2021","unstructured":"Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate. 2021. Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In 26th International Conference on Intelligent User Interfaces. 340\u2013350."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359204"},{"key":"e_1_3_2_1_83_1","volume-title":"Jennifer Wortman Vaughan, and Hanna Wallach","author":"Poursabzi-Sangdeh Forough","year":"2018","unstructured":"Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018)."},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173677"},{"key":"e_1_3_2_1_85_1","unstructured":"UCI Mahcine Learning Repository. 1994. Adult Data Set."},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00307-0"},{"key":"e_1_3_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-010-0066-7"},{"key":"e_1_3_2_1_90_1","volume-title":"Predicting recidivism in north carolina","author":"Schmidt Peter","year":"1978","unstructured":"Peter Schmidt and Ann D Witte. 1988. Predicting recidivism in north carolina, 1978 and 1980. Inter-university Consortium for Political and Social Research."},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376624"},{"key":"e_1_3_2_1_92_1","volume-title":"Computer Graphics Forum","author":"Sperrle Fabian","unstructured":"Fabian Sperrle, Mennatallah El-Assady, Grace Guo, Rita Borgo, D Horng Chau, Alex Endert, and Daniel Keim. 2021. A Survey of Human-Centered Evaluations in Human-Centered Machine Learning. In Computer Graphics Forum, Vol. 40. Wiley Online Library, 543\u2013567."},{"key":"e_1_3_2_1_93_1","volume-title":"Alex Endert, and Daniel Keim.","author":"Sperrle Fabian","year":"2020","unstructured":"Fabian Sperrle, Mennatallah El-Assady, Grace Guo, Duen Horng Chau, Alex Endert, and Daniel Keim. 2020. Should We Trust (X) AI? Design Dimensions for Structured Experimental Evaluations. arXiv preprint arXiv:2009.06433 (2020)."},{"key":"e_1_3_2_1_94_1","volume-title":"Progressive disclosure: designing for effective transparency. arXiv preprint arXiv:1811.02164","author":"Springer Aaron","year":"2018","unstructured":"Aaron Springer and Steve Whittaker. 2018. Progressive disclosure: designing for effective transparency. arXiv preprint arXiv:1811.02164 (2018)."},{"key":"e_1_3_2_1_95_1","unstructured":"Lending Club Statistics. 2019. Lending Club."},{"key":"e_1_3_2_1_96_1","volume-title":"Advances in Human Factors in Robots and Unmanned Systems","author":"Stowers Kimberly","unstructured":"Kimberly Stowers, Nicholas Kasdaglis, Michael Rupp, Jessie Chen, Daniel Barber, and Michael Barnes. 2017. Insights into human-agent teaming: Intelligent agent transparency and uncertainty. In Advances in Human Factors in Robots and Unmanned Systems. Springer, 149\u2013160."},{"key":"e_1_3_2_1_97_1","unstructured":"Supreme Court of Wisconsin. 2016. State of Wisconsin Plaintiff-Respondent v. Eric L. Loomis Defendant-Appellant."},{"key":"e_1_3_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397481.3450662"},{"key":"e_1_3_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445101"},{"key":"e_1_3_2_1_100_1","volume-title":"Office of Justice Programs","author":"United States Department of Justice.","year":"2014","unstructured":"United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics.2014. State Court Processing Statistics, 1990-2009: Felony Defendants in Large Urban Counties."},{"key":"e_1_3_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445365"},{"key":"e_1_3_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174014"},{"key":"e_1_3_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3476068"},{"key":"e_1_3_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654940"},{"key":"e_1_3_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397481.3450650"},{"key":"e_1_3_2_1_106_1","volume-title":"A human-grounded evaluation of shap for alert processing. arXiv preprint arXiv:1907.03324","author":"Weerts Hilde JP","year":"2019","unstructured":"Hilde JP Weerts, Werner van Ipenburg, and Mykola Pechenizkiy. 2019. A human-grounded evaluation of shap for alert processing. arXiv preprint arXiv:1907.03324 (2019)."},{"key":"e_1_3_2_1_107_1","volume-title":"A Survey of Human-in-the-loop for Machine Learning. arXiv preprint arXiv:2108.00941","author":"Wu Xingjiao","year":"2021","unstructured":"Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. 2021. A Survey of Human-in-the-loop for Machine Learning. arXiv preprint arXiv:2108.00941 (2021)."},{"key":"e_1_3_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377480"},{"key":"e_1_3_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2016.1200981"},{"key":"e_1_3_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300509"},{"key":"e_1_3_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302277"},{"key":"e_1_3_2_1_112_1","volume-title":"Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. arXiv preprint arXiv:2001.02114","author":"Zhang Yunfeng","year":"2020","unstructured":"Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. arXiv preprint arXiv:2001.02114 (2020)."}],"event":{"name":"FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency","location":"Chicago IL USA","acronym":"FAccT '23"},"container-title":["2023 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3593013.3594087","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3593013.3594087","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:19Z","timestamp":1750178239000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3593013.3594087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,12]]},"references-count":112,"alternative-id":["10.1145\/3593013.3594087","10.1145\/3593013"],"URL":"https:\/\/doi.org\/10.1145\/3593013.3594087","relation":{},"subject":[],"published":{"date-parts":[[2023,6,12]]},"assertion":[{"value":"2023-06-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}