Algorithmic Responsibility

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Gamification in HR

Gamification is an emerging innovation in talent identification and HR analytics. It uses game design principles and algorithms to engage, motivate, and retain current or potential employees. Experts at Gartner believe that “humans are hard-wired to enjoy games and have a natural tendency to interact more deeply in activities that are framed in a game construct,” which is why companies can benefit from gamification as it uses techniques from behavioral science to “nudge” people into achieving their goals (2021). There are many ways to use gamification in HR. In talent identification, interactive assessments can evaluate a candidate's skills and abilities through game-like activities. For example, the US Air Force uses a video game-like reality simulation called the Airman Challenge to test problem-solving skills or how well individuals can navigate difficult situations that arise in the life of an airman (2023). Assessments like these can provide a more engaging and accurate evaluation of a candidate's potential, while creating a positive experience that showcases the company's employer brand, adding to the employee value proposition.

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Additionally, gamification in the employee life cycle can encourage them to participate in training and learning, and development programs. This can also encourage collaboration among teams through group challenges or friendly competition. For example, an organization can create a leaderboard to track employee progress in completing learning and development modules, with rewards and recognition given to those individuals or teams with the highest scores. Another example would be using virtual or augmented reality, such as Metaverse, to train employees for professions that require practical or safety training, such as construction. This approach can increase engagement and retention while also providing valuable data on employee performance and skill development, allowing HR professionals to make better decisions (Pittsburg State University, 2021).

 

While there are many uses of gamification in HR, it is important to understand what powers it. Aside from game-designing principles such as objectivity or rewards, gamification uses algorithms to track employee progress, provide feedback, and make recommendations for improvement. These algorithms use analytics to examine data collected from the games to identify patterns and trends in employee behavior and performance, allowing each employee to get personalized feedback that they can leverage for growth and career development. A great example is the gamified learning management system offered by SAP SuccessFactors. It uses algorithms to make personalized recommendations for training based on an individual’s role, interests, and career goals. Additionally, it also gives feedback on areas of improvement based on their progress (Wueest, 2020). This is done by analyzing responses and time spent on questions. With so many possibilities of gamifying HR processes such as recruitment, training, and learning come multiple benefits and challenges. Let us look at the two in comparison.

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Benefits and Challenges

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Algorithmic Risks and Recommendations

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While algorithms have the potential to be the least biased, organizations must be aware of the potential risks associated with using algorithms in gamification and HR. In some cases, algorithms have been intercepted for biases that reinforced existing inequalities. For example, Amazon used to use an AI tool for recruitment that showed bias against women (Reuters, 2018). As Cathy O’Neil writes in her book, Weapons of Math Destruction, “a model that works for one group of people may fail miserably for another. But we’re often too enamored of the model to ask the right questions” (2017). Algorithms should be designed to represent what the data should look like and not what it is because current data is subject to biases due to human intervention. Companies should ensure that their algorithms are transparent and explainable in order to mitigate these risks and avoid black-box situations that can also result in lawsuits. It will also be helpful to monitor and evaluate the gamified processes for fairness and accuracy. Data governance policies should be put in place to have clear guidelines. These guidelines should highlight not only what must be done but also what must not be done. With these precautions in place, the benefits of using gamification and algorithms in HR analytics can be fully realized.

Gamification of HR has many uses and benefits that can help enhance the employee value proposition and life cycle. If designed and used correctly, algorithmic-based gamified learning or training can create a positive impact. However, it is important to remember that "the more data we have, the more we trust the algorithms, and the less likely we are to question them” (O’neil, 2017). Companies should invest in such technologies but also not used it to totally replace human expertise and should always question the validity, source, and scalability.

 

References

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Airman Challenge. (2023). From https://www.airforce.com/airmanchallenge/fallback.html

 

Bush, S. (2022, September 20). Beware the rise of the black box algorithm. Financial Times. https://www.ft.com/content/3d5556c5-520e-497a-aa5e-2546c5bc50cf

 

Gamification. (2021). In Gartner Glossary. From https://www.gartner.com/en/marketing/glossary/gamification

 

LinkedIn Learning. (2021). How personalized learning enhances employee development. LinkedIn Learning Resources. https://learning.linkedin.com/resources/learner-engagement/how-personalized-learning-enhance s-employee-development

 

O'Neil, C. (2017). Weapons of Math Destruction. Penguin Books. Pittsburg State University. (2021). Gamification in HR. from

https://degree.pittstate.edu/online-programs/mba/human-resources/gamification-in-hr/

 

Reuters. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret- ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

 

SHRM. (2022, April 25). SHRM research: AI uses rise; ethics questions remain. SHRM.

https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/shrm-research-ai-use-rise- ethics-questions-remain.aspx

 

WeSoar. (2022, February 28). How gamification can improve employee performance. LinkedIn.

https://www.linkedin.com/pulse/how-gamification-can-improve-employee-performance-wesoar?tr k=organization-update-content_share-article

 

Wueest, D. (2020, August 20). Benefits of Gamification with SAP SuccessFactors LMS: Make Learning a Game. SAP Blogs. from https://blogs.sap.com/2020/08/20/benefits-of-gamification-with-sap-successfactors-lms-make-learning-a-game/

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Reflection

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Learning about Algorithmic Responsibility in the HCAT program at NYU helped me understand the nuances of algorithms and their uses across the platform be it to identify candidates for a role using assessments such as Core Drivers or using it to create performance management plans for employees. This class also enhanced my understanding of HR Analytics and what a future professional in the field should consider will applying AI or other tools in the workplace. 
 

One of the key concepts in algorithmic responsibility is the idea of bias. Algorithms can be biased in many ways, including race, gender, and age. By understanding how bias can manifest in algorithms, one can take steps to identify and mitigate it in HR analytics. For example, analyzing the data used to train the algorithm and identifying any biases in the data. Alternatively, we can modify the algorithm to ensure that it produces unbiased results. Another important aspect of algorithmic responsibility is transparency. In HR analytics, it is essential to be transparent about how algorithms are used and the data they rely on. This allows employees to understand how decisions are being made and to identify any potential biases or errors. Additionally, transparency can build trust between employers and employees, which can be essential for success in HR analytics. Finally, algorithmic responsibility can contribute to a broader understanding of ethics in HR analytics. HR analytics is not only about making data-driven decisions but also about doing so in a way that is ethical and responsible. This means taking into account factors such as privacy, consent, and fairness. 

 

I really enjoyed reading Weapons of Math Destruction by Cathy O'Neil and then discussing our views with the class. Important concepts such as validity, reliability, and scalability came up in these discussions, allowing me to gain a broad perspective on the topic. I also enjoyed the group presentation aspect of the class. It allowed me to explore the ethics and legality of algorithms in depth and present my views to the class. 

I am grateful to Uri and Reece for the knowledge and insights they passed down to the class. I am excited to explore the world of HR Analytics and the learnings from this class will always be ingrained in my practice as an HR Professional.