Spare me the details: How the type of information about automated interviews influences applicant reactions

Langer, Markus; Baum, Kevin; König, Cornelius J.; Hähne, Viviane; Oster, Daniel; Speith, Timo

doi:10.1111/ijsa.12325

Cited by 25 publications

(27 citation statements)

References 51 publications

(94 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, such an explanation-based taxonomy could help people from these disciplines to get started with XAI. For instance, utilizing such a taxonomy, psychologists could conduct studies to find out which kinds of explanations are best suited for certain contexts (see, e.g., [42,43,71] for such kinds of studies in hiring scenarios).…”

Section: Taxonomies For Scholars From Outside Of Computer Sciencementioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

Self Cite

135

View full text Add to dashboard Cite

The recent surge in publications related to explainable artificial intelligence (XAI) has led to an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this reason, articles and reviews that present taxonomies of XAI methods seem to be a welcomed way to get an overview of the field. Building on this idea, there is currently a trend of producing such taxonomies, leading to several competing approaches to construct them. In this paper, we will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations. Our review is intended to help scholars be aware of challenges current taxonomies face. As we will argue, when charting the field of XAI, it may not be sufficient to rely on one of the approaches we found. To amend this problem, we will propose and discuss three possible solutions: a new taxonomy that incorporates the reviewed ones, a database of XAI methods, and a decision tree to help choose fitting methods. CCS CONCEPTS• General and reference → Surveys and overviews; • Computing methodologies → Artificial intelligence.

show abstract

Section: Taxonomies For Scholars From Outside Of Computer Sciencementioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

Self Cite

135

View full text Add to dashboard Cite

show abstract

“…For instance, one can expect this to be the case in the areas of health (Juravle et al, 2020), but also in other settings where it is not the individual but the state who uses AI such as in tax fraud detection (Kieslich et al, 2021) or in the criminal justice system (Waggoner et al, 2019). Similarly, transparency may be valued more where individuals are not the users of a service but the object, as in recruiting decisions (Langer et al, 2021). The value of transparency may thus well rise proportional to the severity of the impacts that individual AI system decisions potentially have.…”

Section: Discussionmentioning

confidence: 99%

Consumers are willing to pay a price for explainable, but not for green AI. Evidence from a choice-based conjoint analysis

2022

View full text Add to dashboard Cite

A major challenge with the increasing use of Artificial Intelligence (AI) applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from a choice-based conjoint analysis with a sample of more than 1.000 respondents from Germany indicate that people hardly care about the energy efficiency of AI; and while they do value transparency through explainable AI, this added value of an application is offset by minor costs. The findings shed light on what kinds of AI people are likely to demand and have important implications for policy and regulation.

show abstract

“…In fact, both quantity and quality of explanations matter: Kulesza et al [31] explored the effects of soundness and completeness of explanations on end users' mental models and suggest, among others, that oversimplification is problematic. Recent findings from Langer et al [34], on the other hand, suggest that in certain cases it might make sense to withhold pieces of information in order to not evoke negative reactions. Either way, even in the presence of explanations, people sometimes rely too heavily on system suggestions [5], a phenomenon sometimes referred to as automation bias [13,19].…”

Section: Key Related Work and Research Gapsmentioning

confidence: 99%

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Schoeffer

2022

CHI Conference on Human Factors in Computing Systems Extended Abstracts

View full text Add to dashboard Cite

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place-which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans; (b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS; and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Machine learning; • Information systems → Decision support systems.

show abstract

Spare me the details: How the type of information about automated interviews influences applicant reactions

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 25 publications

References 51 publications

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Consumers are willing to pay a price for explainable, but not for green AI. Evidence from a choice-based conjoint analysis

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Contact Info

Product

Resources

About