The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice
Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human–machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different...
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| Veröffentlicht in: | Production and operations management Jg. 31; H. 9; S. 3419 - 3434 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Hoboken, NJ
Wiley
01.09.2022
SAGE Publications Blackwell Publishers Inc |
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| ISSN: | 1937-5956, 1059-1478, 1937-5956 |
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| Abstract | Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human–machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different degrees of complexity. We conduct a set of laboratory experiments in which participants receive identical advice from algorithms with different levels of transparency and complexity. Our results indicate that not the algorithm itself, but the individually perceived appropriateness of algorithmic complexity moderates the effects of transparency on the use of advice. We summarize this effect as a plateau curve: While perceiving an algorithm as too simple severely harms the use of its advice, the perception of an algorithm as being too complex has no significant effect. Our insights suggest that managers do not have to be concerned about revealing algorithms that are perceived to be appropriately complex or too complex to decision‐makers, even if the decision‐makers do not fully comprehend them. However, providing transparency on algorithms that are perceived to be simpler than appropriate could disappoint people's expectations and thereby reduce the use of their advice. |
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| AbstractList | Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human–machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different degrees of complexity. We conduct a set of laboratory experiments in which participants receive identical advice from algorithms with different levels of transparency and complexity. Our results indicate that not the algorithm itself, but the individually perceived appropriateness of algorithmic complexity moderates the effects of transparency on the use of advice. We summarize this effect as a plateau curve: While perceiving an algorithm as too simple severely harms the use of its advice, the perception of an algorithm as being too complex has no significant effect. Our insights suggest that managers do not have to be concerned about revealing algorithms that are perceived to be appropriately complex or too complex to decision‐makers, even if the decision‐makers do not fully comprehend them. However, providing transparency on algorithms that are perceived to be simpler than appropriate could disappoint people's expectations and thereby reduce the use of their advice. Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human–machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different degrees of complexity. We conduct a set of laboratory experiments in which participants receive identical advice from algorithms with different levels of transparency and complexity. Our results indicate that not the algorithm itself, but the individually perceived appropriateness of algorithmic complexity moderates the effects of transparency on the use of advice. We summarize this effect as a plateau curve: While perceiving an algorithm as too simple severely harms the use of its advice, the perception of an algorithm as being too complex has no significant effect. Our insights suggest that managers do not have to be concerned about revealing algorithms that are perceived to be appropriately complex or too complex to decision‐makers, even if the decision‐makers do not fully comprehend them. However, providing transparency on algorithms that are perceived to be simpler than appropriate could disappoint people's expectations and thereby reduce the use of their advice. |
| Author | Lehmann, Cedric A. Fügener, Andreas Haubitz, Christiane B. Thonemann, Ulrich W. |
| Author_xml | – sequence: 1 givenname: Cedric A surname: Lehmann fullname: Lehmann, Cedric A – sequence: 2 givenname: Christiane B surname: Haubitz fullname: Haubitz, Christiane B – sequence: 3 givenname: Andreas surname: Fügener fullname: Fügener, Andreas – sequence: 4 givenname: Ulrich W surname: Thonemann fullname: Thonemann, Ulrich W |
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| Keywords | use of advice decision‐making decision support algorithm complexity algorithm transparency |
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an investigation from perception to decision – start-page: 258 year: 2019 end-page: 262 article-title: The effects of example‐based explanations in a machine learning interface – volume: 22 start-page: 415 issue: 3 year: 2006 end-page: 432 article-title: The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion publication-title: International Journal of Forecasting – year: 2021 article-title: Will we trust what we don't understand? Impact of model interpretability and outcome feedback on trust in AI – volume: 25 start-page: 3 issue: 1 year: 2009 end-page: 23 article-title: Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply‐chain planning publication-title: International Journal of Forecasting – volume: 13 start-page: 75 issue: 2 year: 2004a end-page: 78 article-title: The benefit of additional opinions publication-title: Current Directions in Psychological Science – volume: 144 start-page: 114 issue: 1 year: 2015 end-page: 126 article-title: Algorithm aversion: People erroneously avoid algorithms after seeing them err publication-title: Journal of Experimental Psychology: General – volume: 57 start-page: 1827 issue: 10 year: 2011 end-page: 1843 article-title: Demand forecasting behavior: System neglect and change detection publication-title: Management Science – volume: 52 start-page: 381 issue: 3 year: 2010 end-page: 410 article-title: Complacency and bias in human use of automation: An attentional integration publication-title: Human Factors – volume: 20 start-page: 21 issue: 1 year: 2007 end-page: 35 article-title: Effects of task difficulty on use of advice publication-title: Journal of Behavioral Decision Making – volume: 23 start-page: 217 issue: 4 year: 2007 end-page: 246 article-title: Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs publication-title: Journal of Management Information Systems – volume: 35 start-page: 780 issue: 3 year: 2009 end-page: 805 article-title: Strategies for revising judgment: How (and how well) people use others' opinions publication-title: Journal of Experimental Psychology: Learning, Memory and, Cognition – volume: 18 start-page: 455 issue: 5 year: 2008 end-page: 496 article-title: The effects of transparency on trust in and acceptance of a content‐based art recommender publication-title: User Modeling and User‐Adapted Interaction – volume: 62 start-page: 451 issue: 1 year: 2011 end-page: 482 article-title: Heuristic decision making publication-title: Annual Review of Psychology – volume: 52 start-page: 16 issue: 1 year: 2019 end-page: 26 article-title: Redefining data transparency: A multidimensional approach publication-title: Computer – year: 2014 – volume: 9 start-page: 88 year: 2016 end-page: 97 article-title: otree—An open‐source platform for laboratory, online, and field experiments publication-title: Journal of Behavioral and Experimental Finance – volume: 25 start-page: 63 issue: 2 year: 2018 end-page: 72 article-title: Asking ‘why’ in AI: Explainability of intelligent systems ‐ perspectives and challenges publication-title: Intelligent Systems in Accounting, Finance and Management – volume: 22 start-page: 3055 issue: 19 year: 2003 end-page: 3071 article-title: Estimating regression models with unknown break‐points publication-title: Statistics in Medicine – volume: 183 start-page: 771 issue: 3 year: 2020 end-page: 800 article-title: Simple rules to guide expert classifications publication-title: Journal of the Royal Statistical Society: Series A (Statistics in Society) – volume: 56 start-page: 809 issue: 5 year: 2019 end-page: 825 article-title: Task‐dependent algorithm aversion publication-title: Journal of Marketing Research – start-page: 2390 year: 2016 end-page: 2395 article-title: How much information? Effects of transparency on trust in an algorithmic interface – volume: 58 start-page: 697 issue: 6 year: 2003 end-page: 718 article-title: The role of trust in automation reliance publication-title: International Journal of Human‐Computer Studies – volume: 14 start-page: 627 issue: 2 year: 2020 end-page: 660 article-title: Human trust in artificial intelligence: Review of empirical research publication-title: Academy of Management Annals – volume: 12 start-page: 91 issue: 1 year: 1996 end-page: 118 article-title: Judgemental and statistical time series forecasting: A review of the literature publication-title: International Journal of Forecasting – start-page: 1 year: 2020 end-page: 9 article-title: Keep it mystic? ‐ The effects of algorithm transparency on the use of advice – volume: 62 start-page: 537 issue: 3 year: 2011 end-page: 543 article-title: Experts' adjustment to model‐based SKU‐level forecasts: Does the forecast horizon matter? publication-title: Journal of the Operational Research Society – volume: 36 start-page: 5 year: 2015 end-page: 12 article-title: Improving forecast quality in practice publication-title: Foresight: The International Journal of Applied Forecasting – volume: 22 start-page: 493 issue: 3 year: 2006 end-page: 518 article-title: Judgmental forecasting: A review of progress over the last 25 years publication-title: International Journal of Forecasting – volume: 93 start-page: 1 issue: 1 year: 2004b end-page: 13 article-title: Receiving other people's advice: Influence and benefit publication-title: Organizational Behavior and Human Decision Processes – volume: 101 start-page: 127 issue: 2 year: 2006 end-page: 151 article-title: Advice taking and decision‐making: An integrative literature review, and implications for the organizational sciences publication-title: Organizational Behavior and Human Decision Processes – volume: 12 start-page: 37 issue: 1 year: 1999 end-page: 53 article-title: Judgmental forecasts of time series affected by special events: Does providing a statistical forecast improve accuracy? publication-title: Journal of Behavioral Decision Making – year: 2022 article-title: Evaluating human behaviour in response to ai recommendations for judgemental forecasting – volume: 68 start-page: 2949 issue: 4 year: 2021 end-page: 2969 article-title: The bad thing about good advice: Understanding when and how advice exacerbates overconfidence publication-title: Management Science – volume: 86 start-page: 237 year: 2019 end-page: 252 article-title: Integrating human judgement into quantitative forecasting methods: A review publication-title: Omega – volume: 107 start-page: 234 issue: 2 year: 2008 end-page: 245 article-title: Do we listen to advice just because we paid for it? The impact of advice cost on its use publication-title: Organizational Behavior and Human Decision Processes – start-page: 1 year: 2019 end-page: 5 article-title: Making transparency clear – volume: 36 start-page: 691 issue: 6 year: 2017 end-page: 702 article-title: Understanding algorithm aversion: When is advice from automation discounted? publication-title: Journal of Forecasting – volume: 48 start-page: 1521 issue: 9 year: 2008 end-page: 1535 article-title: Average behavior of greedy algorithms for the minimization knapsack problem: General coefficient distributions publication-title: Computational Mathematics and Mathematical Physics – volume: 35 start-page: 144 issue: 1 year: 2019 end-page: 156 article-title: Use and misuse of information in supply chain forecasting of promotion effects publication-title: International Journal of Forecasting – year: 2020 article-title: Good explanation for algorithmic transparency – volume: 74 start-page: 33 issue: 1 year: 1987 end-page: 43 article-title: Hypothesis testing when a nuisance parameter is present only under the alternative publication-title: Biometrika – volume: 17 start-page: 173 issue: 3 year: 2004 end-page: 190 article-title: Improving judgement with prepaid expert advice publication-title: Journal of Behavioral Decision Making – volume: 521 start-page: 436 year: 2015 end-page: 444 article-title: Deep learning publication-title: Nature – volume: 70 start-page: 117 issue: 2 year: 1997 end-page: 133 article-title: Taking advice: Accepting help, improving judgment, and sharing responsibility publication-title: Organizational Behavior and Human Decision Processes – volume: 151 start-page: 90 year: 2019 end-page: 103 article-title: Algorithm appreciation: People prefer algorithmic to human judgment publication-title: Organizational Behavior and Human Decision Processes – volume: 45 start-page: 1527 issue: 3 year: 2021 end-page: 1556 article-title: Will humans‐in‐the‐loop become borgs? Merits and pitfalls of working with AI publication-title: Management Information Systems Quarterly – volume: 29 start-page: 354 issue: 2 year: 2013 end-page: 366 article-title: Antecedents and effects of trust in forecasting advice publication-title: International Journal of Forecasting – volume: 37 start-page: 570 issue: 6 year: 2007 end-page: 576 article-title: Against your better judgment? How organizations can improve their use of management judgment in forecasting publication-title: Interfaces – volume: 20 start-page: 1 year: 2019 end-page: 75 article-title: Learning optimized risk scores publication-title: Journal of Machine Learning Research – volume: 15 start-page: 227 issue: 3 year: 1999 end-page: 246 article-title: Combining forecasts: What information do judges need to outperform the simple average? publication-title: International Journal of Forecasting – volume: 12 start-page: 119 issue: 1 year: 1996 end-page: 137 article-title: Graphs versus tables: Effects of data presentation format on judgemental forecasting publication-title: International Journal of Forecasting – volume: 33 start-page: 220 issue: 2 year: 2020 end-page: 239 article-title: A systematic review of algorithm aversion in augmented decision‐making publication-title: Journal of Behavioral Decision Making – volume: 274 start-page: 574 issue: 2 year: 2019 end-page: 600 article-title: The human factor in supply chain forecasting: A systematic review publication-title: European Journal of Operational Research – volume: 5 start-page: 266 issue: 2 year: 1957 end-page: 277 article-title: Discrete‐variable extremum problems publication-title: Operations Research – volume: 25 start-page: 7 year: 2018 article-title: Detecting bias: Does an algorithm have to be transparent in order to be fair? 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| SubjectTerms | algorithm complexity algorithm transparency Algorithms decision support decision‐making use of advice |
| Title | The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice |
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