Implications of AI (un-)fairness in higher education admissions

Marcinkowski, Frank; Kieslich, Kimon; Starke, Christopher; Lünich, Marco

doi:10.1145/3351095.3372867

Cited by 90 publications

(51 citation statements)

References 30 publications

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“…However, decision-making tasks can also be found in other domains of application. For example, in medicine AI systems could directly decide over medical treatments of patients; in the higher education sector ADM could decide about the admissions of students' applications to university [40]. Concerning the human-computer interaction an ADM substitutes the human task completely.…”

Section: Decision-makingmentioning

confidence: 99%

The Threats of Artificial Intelligence Scale (TAI)

Kieslich

Lünich

Marcinkowski

2021

Int J of Soc Robotics

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In recent years Artificial Intelligence (AI) has gained much popularity, with the scientific community as well as with the public. Often, AI is ascribed many positive impacts for different social domains such as medicine and the economy. On the other side, there is also growing concern about its precarious impact on society and individuals, respectively. Several opinion polls frequently query the public fear of autonomous robots and artificial intelligence, a phenomenon coming also into scholarly focus. As potential threat perceptions arguably vary with regard to the reach and consequences of AI functionalities and the domain of application, research still lacks necessary precision of a respective measurement that allows for wide-spread research applicability. We propose a fine-grained scale to measure threat perceptions of AI that accounts for four functional classes of AI systems and is applicable to various domains of AI applications. Using a standardized questionnaire in a survey study (N = 891), we evaluate the scale over three distinct AI domains (medical treatment, job recruitment, and loan origination). The data support the dimensional structure of the proposed Threats of AI (TAI) scale as well as the internal consistency and factoral validity of the indicators. Implications of the results and the empirical application of the scale are discussed in detail. Recommendations for further empirical use of the TAI scale are provided.

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Section: Decision-makingmentioning

confidence: 99%

The Threats of Artificial Intelligence Scale (TAI)

Kieslich

Lünich

Marcinkowski

2021

Int J of Soc Robotics

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“…Studies revealed contradicting results as to what is perceived as fair. People perceive algorithmic decision-making as less fair than human decision-making even when the decision requires 'human skills' [32] or more fair in other contexts, like school admissions [35]. Algorithms and systems should consider social and altruistic behavior in order to be considered as fair, elements that may be difficult to incorporate in mathematical modelling [8].…”

Section: Fairness In Algorithmic Decision Makingmentioning

confidence: 99%

“…The study received ethical clearance from the national ethics committee of the country where the authors' institution is operating. 4 60.6% of our respondents were male, with 47.5% in the age group of [18][19][20][21][22][23][24]35.4% between 25-32, 10.1% between 33-40, and 7.1% above 40 years old. Most of the participants (68.7%) identified themselves as a postgraduate student, and 54% of that group were Master's students.…”

Section: Participantsmentioning

confidence: 99%

I agree with the decision, but they didn't deserve this

Kasinidou

Kleanthous

Barlas

et al. 2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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While professionals are increasingly relying on algorithmic systems for making a decision, on some occasions, algorithmic decisions may be perceived as biased or not just. Prior work has looked into the perception of algorithmic decision-making from the user's point of view. In this work, we investigate how students in fields adjacent to algorithm development perceive algorithmic decisionmaking. Participants (N=99) were asked to rate their agreement with statements regarding six constructs that are related to facets of fairness and justice in algorithmic decision-making in three separate scenarios. Two of the three scenarios were independent of each other, while the third scenario presented three different outcomes of the same algorithmic system, demonstrating perception changes triggered by different outputs. Quantitative analysis indicates that ) 'agreeing' with a decision does not mean the person 'deserves the outcome', ) perceiving the factors used in the decision-making as 'appropriate' does not make the decision of the system 'fair' and ) perceiving a system's decision as 'not fair' is affecting the participants' 'trust' in the system. In addition, participants found proportional distribution of benefits more fair than other approaches. Qualitative analysis provides further insights into that information the participants find essential to judge and understand an algorithmic decision-making system's fairness. Finally, the level of academic education has a role to play in the perception of fairness and justice in algorithmic decision-making.

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“…In particular, when students consider educational systems as unfair or discriminatory then they lose their trust mainly, motivation, satisfaction and even their commitment to the university. Students' perceived inadequacy of justice triggers their tendency to dropout of university or the decision to continue their studies elsewhere (Marcinkowski et al, 2020).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

The Role of the Academic Factor in Student’s Tendency to University Dropout

Androulakis¹,

Georgiou²,

Kiprianos³

et al. 2021

JEHD

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Dropout in higher education has got important dimensions worldwide. The problem has consequences on an individual, social and economic level and it is thus of significance to investigate students' tendency to dropout before the decision to leave their studies is irreversible. Due to the complexity of the dropout phenomenon and the diversity of the influence exerted by different factors, arises intensively the need for a thorough comprehension of the mechanism and decomposition of the exerted intercorrelations. In regards to the above, this research aimed to analyze the effect of the variables within the academic spectrum on students' tendency to dropout of their studies via hierarchical regression trees indicating both the sequence and intercorrelation. From the present work it emerged that the elements of the academic index exert a significant impact on students' tendency to dropout via the sequence of level of study followed by the subject of study, the provision of knowledge to solve complex real-world problems, the feedback by professors on students' work completed, the students' perception of justice and reward in the evaluation procedures concerning them, the clarity of the educational goals and courses' requirements as well as students' satisfaction with the work of the teaching staff.

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Implications of AI (un-)fairness in higher education admissions

Cited by 90 publications

References 30 publications

The Threats of Artificial Intelligence Scale (TAI)

The Threats of Artificial Intelligence Scale (TAI)

I agree with the decision, but they didn't deserve this

The Role of the Academic Factor in Student’s Tendency to University Dropout

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