## 7 AI Fairness in Practice
### Business
#### 7.2.1 Hiring and Recruiting  
The integration of artificial intelligence (AI) in human resource management (HRM) introduces transformative enhancements. Figure 3 shows a recruitment process supported by AI throughout. AI-driven algorithms streamline CV screening and candidate profiling, while protored assessments ensure secure remote testing. AI optimizes interview scheduling and personalizes HR training. Behavior tracking and personality analysis provide insights into candidate dynamics, and AI aids in appraisal monitoring through performance metric analysis. These applications collectively reshape HRM practices, enhancing efficiency and informed decision making.  
In the applications of AI system in HRM, artificial intelligence fairness is applied to mitigate bias in automated hiring systems, ensuring equitable and non-discriminatory candidate selection [305; 306]. Many AI-driven recruitment tools have exhibited biases, favoring specific candidates and overlooking job requisites [307; 308]. Utilizing AI fairness techniques rectifies these model biases, fostering impartial hiring decisions independent of attributes unrelated to job proficiency [309]. Tackling these challenges and future endeavors in AI fairness is pivotal to harnessing the potential of AI for equitable recruitment, fostering diversity and inclusivity in workforce dynamics.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Issues** & **Mechanism** & **Opportunities** & **Challenges** \\ \hline \multirow{4}{*}{Recruiting} & Bias in job ads and candidate selection. Lack of of diversity in hiring. & Debiasing job descriptions, candidate screening and removing identifiable information, diversifying training data. & Increasing workforce diversity. Reducing hiring discrimination. & Balancing fairness and competence. Ensuring fairness across different demographics. \\ \hline \multirow{4}{*}{Lending and Credit Decisions} & Discrimination in loan approvals. Lack of transparency in decision making. & Implementing fairness-aware algorithms, explaining model decisions, alternative data to creditworthiness. & Expanding access to credit for marginalized groups. Improving overall lending practices. & Striking a balance between fairness and risk assessment. Handling potential adversarial attacks on models. \\ \hline \multirow{4}{*}{Online advertising} & Targeting ads based on sensitive attributes. & Differential privacy to protect privacy, biased message screening, providing users preference controls. & Improving user experience and privacy protection. Fostering a positive brand image. & The balance between targeted ads and user privacy. Identifying and Addressing hidden biases in ad delivery. \\ \hline \multirow{4}{*}{Customer Service and Chatbots} & biased responses and inappropriate interactions. & Training chatbots on inclusive and diverse datasets with reinforcement learning to improve interactions with feedback on bot behavior. & Enhancing user experience and customer satisfaction. Scaling customer support efficiently. & Minimizing harmful or offensive responses. Dealing with novel inputs and out-of-distribution data. \\ \hline \hline \end{tabular}
\end{table}
Table 14: AI fairness in business practices.