Hi, welcome to the presentation of our paper on human AI interaction in human resource management.
i'm Hyanghee Park from Seoul National University. I'm presenting this paper on behalf of my co-authors
Daehwan and Kartik Hosanagar from the Wharton School and my advisor Joonhwan Lee.
The advent of artificial intelligence is changing our workplaces through efficient and data-driven
decision-making. In the case of Uber, AI assigns drivers which passenger to pick up and Amazon
Mechanical Turk workers often work with algorithms. In recent years, AI has gradually
been applied to human resource management, predominantly in recruitment. Ai is being used
for resume screening, employment skill testing, and even picks out top talent software developers.
Notably between 2017 and 2018, Amazon surprised the world by allowing the AI
to make decisions of job termination. Despite the pervasive utilization of AI in the field
of HRM, understanding its legitimacy and labors' perceptions is still in the exploration stage.
An employee's perception of an algorithmic decision in HRM seems complicated and inconsistent.
For example, recent surveys reported that employees trust AI over their managers
However, when amazon automatically tracked and fired its workers through AI, its union accused
the company in protest. Then, what makes the Amazon workers sue their company for utilizing algorithms
for decision making, whereas other employees say that they can trust AI over their human managers.
In this paper, we investigate why employees resist algorithmic evaluation at workplaces
and how to mitigate burdens. To explore employees burdens toward the AI, we conducted
scenario-based in-depth interviews with 21 participants who have diverse work experience.
We designed four scenarios based on real world use cases including evaluation for menial work,
physical work, office work, and customer service. These scenarios were shown to participants and
we asked them to describe the first image of the AI, what advantages or disadvantages AI would bring,
what AI features are needed to accept the work performance evaluation and etc.
Regarding employees antipathy toward algorithmic management, we discovered six types of burdens
including emotional, mental, social, bias, privacy, and manipulation burden. To mitigate employees burdens,
participants requested three major AI design factors. First, participants called for transparency
in AI regarding the evaluation criteria and operation process. However, it is critical to
achieve not only algorithm transparency, but also a high level of process transparency.
Participants strongly asserted that AI should explain the reasons behind decisions on work
performance evaluations. We suggest ai designers in HRM provide interpretability including not only
the traditional statistical interpretability but also human-like explanation for human-like
interaction. A total of 17 participants preferred human-AI collaboration over human or machine
oriented evaluation. This indicated that people prefer neither the traditional evaluation
nor a radical change of the evaluation system. Especially, employees emphasized the role of
mutual surveillance for designing human-AI collaboration in the specific context of HRM.
This is because enormous power shouldn't be concentrated in either the AI or the humans.
Most of the participants wanted to share their emotions with humans not machines, especially when
they received bad results. Thus, AI designers in HRM should pay special attention to empathy design
when AI delivers bad news; employees are averse to AI's empathy when AI is the only
one who badly evaluates them in the first place. This is the brief overview our paper.
Thank you for listening. If you have any questions or comments feel free to contact us.