Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence

We run an online experiment to study the origins of algorithm aversion. Participants are either in the role of workers or of managers. Workers perform three real-effort tasks: task 1, task 2, and the job task which is a combination of tasks 1 and 2. They choose whether the hiring decision between themselves and another worker is made either by a participant in the role of a manager or by an algorithm. In a second set of experiments, managers choose whether they want to delegate their hiring decisions to the algorithm. In the baseline treatments, we observe that workers choose the manager more often than the algorithm, and managers also prefer to make the hiring decisions themselves rather than delegate them to the algorithm. When the algorithm does not use workers' gender to predict their job task performance and workers know this, they choose the algorithm more often. Providing details on how the algorithm works does not increase the preference for the algorithm, neither for workers nor for managers. Providing feedback to managers about their performance in hiring the best workers increases their preference for the algorithm, as managers are, on average, overconfident

A systematic review of artificial intelligence impact assessments

Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI’s benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations’ approaches to AI.</p

A systematic review of artificial intelligence impact assessments

Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI’s benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations’ approaches to AI

A systematic review of artificial intelligence impact assessments

Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI’s benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations’ approaches to AI

Fairness and Bias in Algorithmic Hiring

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders

A importância do people analytics na retenção de talento nas organizações

A retenção de pessoas nas organizações é uma área crucial para a Gestão de Recursos Humanos, estimulando os níveis de produtividade e desempenho, diminuição dos níveis de rotatividade e diminuição dos custos com a saída de colaboradores, gerando maior rentabilidade. O People Analytics surge no sentido de aumentar a contribuição dos Recursos Humanos (RH), nomeadamente, de auxiliar as organizações a tomar decisões críticas informadas em torno da aquisição, desenvolvimento e retenção de talento. Com o objetivo de mapear as contribuições da ciência para o People Analytics na retenção de talento, este estudo apresenta uma revisão sistemática da literatura sobre o tema. Na metodologia utilizada, implementaram-se critérios de seleção que direcionaram a recolha da amostra. A amostra integra 16 documentos científicos escritos por 31 autores, publicados entre 2008 e 2020. Os resultados obtidos inferem sobre as vantagens, o impacto, os desafios e limitações e as Técnicas de Data Mining mais utilizadas no People Analytics na retenção. Este estudo indica que apesar da crescente produção científica do People Analytics, esta ainda é escassa quando aplicada ao domínio da retenção. Identificam-se as seguintes vantagens do People Analytics na retenção: melhorar as práticas de gestão de talento; aumentar o desenvolvimento da tecnologia; a diminuição da taxa de rotatividade e o aumento a competitividade. O estudo indica um impacto positivo da implementação do People Analytics na retenção e aponta como principais desafios, as limitações de sistemas de GRH, a indisponibilidade de dados e a necessidade de formação dos profissionais de RH para aquisição de competências de análise de dados. Quanto às técnicas de data Mining mais utilizadas na retenção, foram identificadas as redes neuronais e as árvores de decisão. Por fim, são discutidas as principais implicações e algumas limitações do estudo, bem como são apresentadas sugestões para pesquisas futuras.Nowadays, effectively managing human capital is fundamental for organizations, which implies the creation of strategies for organizations to gain competitive advantage. People retention in organizations is a crucial area for Human Resources Management, stimulating productivity and performance levels, reducing turnover levels and reducing costs with the departure of employees, generating greater profitability. People Analytics (PA) arises to increase its contribution to HR and has the potential to help organizations make informed critical decisions around the acquisition, development and retention of people. With the objective of mapping the scientific contributions towards People Analytics in people retention, this study presents a systematic literature review on this subject. In the methodology applied, selection criteria were implemented that guided the collection of the sample. The sample includes 16 scientific documents written by 31 authors, published between 2008 and 2020. The results obtained infer about: Advantages of People Analytics in retention; Impact of People Analytics on retention; Challenges and Limitations of People Analytics in retention and Data Mining Techniques most used in retention. This study indicates that despite the increasing scientific production of People Analytics, this is still scarce when applied to retention. The following advantages of People Analytics in retention are identified: improving talent management practices; increase the development of technology; reduce the attrition rate and increase competitiveness. The study indicates a positive impact of the implementation of People Analytics in retention and points out as main challenges, the limitations of HRM systems, the unavailability of data and the need to train HR professionals to acquire data analysis skills. As for the data mining techniques most used in retention, neuronal networks and decision trees were identified. Finally, the main implications and some limitations of the study are discussed, as well as suggestions for future research