Throughout my career, I have been deeply fascinated by the human experience and how people interact with the technology around them. Over the years, I have been lucky enough to work on various design projects, allowing me to explore this topic in depth. From designing user interfaces for software programs to developing new prototypes, my work has been driven by a desire to create products that are genuinely user-centred in their approach.
One of the areas I have focused my research on is the psychology of decision-making. By understanding how people make choices and the factors that influence those choices, I am better able to design products that meet the end user’s needs. This has extended to studying neuroscience research.
As a designer, I have always been aware of the power of design to influence individual behaviour and choices. However, I have found a considerable design gap regarding ethics that needs to be addressed. In my experience, it is not uncommon to encounter situations where design decisions are made solely for profit, with little regard for the long-term impact on the user or society. To bridge this gap, designers must consider the ethical implications of their work and strive to create products that promote the user’s well-being while positively contributing to society.
In the age of information overload, understanding how people make choices has become crucial for designing effective digital products. By delving into the intricate workings of the human mind, I have sought to unravel the psychology behind decision-making. However, AI algorithms have introduced a new dimension to this field. AI-powered nudges, subtle prompts or suggestions, can potentially guide user behaviour and shape decision-making processes (Möhlmann, 2021). These nudges can range from personalized recommendations in e-commerce platforms to persuasive techniques used in social media (Hermann, 2022). As designers, we must harness this power responsibly, ensuring that nudges are designed to benefit users rather than exploit their vulnerabilities (Schmauder et al., 2023).
While AI algorithms can provide remarkable insights and automation, they have shortcomings. As I delved deeper into my research, I discovered the presence of biases within AI algorithms. These biases can emerge due to the data used to train the algorithms, inadvertently perpetuating societal prejudices or exclusionary practices. For instance, biased AI algorithms can lead to discriminatory outcomes in areas such as hiring practices (Raghavan et al., 2020) or healthcare (Daneshjou et al., 2021). As designers, we must actively work to identify and mitigate these biases, striving for fairness, inclusivity, and equity in our products. By doing so, we pave the way for a more just and ethical digital landscape.
The “black box” concept refers to the opaque nature of AI algorithms (Castelvecchi, 2016), where their inner workings remain inaccessible or incomprehensible to users. This lack of transparency raises ethical concerns, as users are unaware of the algorithms’ decision-making processes and their potential impact on their lives. To address this challenge, designers must champion the cause of explainable AI, ensuring that users clearly understand how AI algorithms arrive at their decisions. By demystifying the black box, we empower users to make informed choices, fostering trust and accountability in the realm of digital product design.
Regarding ethical considerations in product design, it’s essential to recognise that every design decision can affect someone’s life. From how we present information to our design choices, each decision can contribute to or detract from the user’s well-being. That’s why it’s essential to think and talk about ethics. By making small ethical choices now, we can build a better honest product design landscape for the future.
During my research, I have encountered numerous instances where a lack of ethical thinking has led to severe consequences for users. These incidents span a variety of industries, including healthcare (Kara, 2022), finance (Wang & Johnson, 2018), and technology (Marshall, 2022). The effects can be devastating, whether the unauthorised use of personal information or the implementation of flawed systems that fail to consider the impact on those who use them. As we move into a more digitally connected world, it has become increasingly clear that ethical considerations must be at the forefront of all decision-making processes.
As we journey towards a more digitally connected future, it is imperative that ethical considerations take centre stage in the design of digital products. We must be mindful of the power we hold as designers, acknowledging that every decision we make can profoundly impact the lives of individuals and society as a whole. We can forge a path towards responsible and ethical design practices by actively engaging with AI-powered nudges, uncovering biases within AI algorithms, and demystifying the black box. Let us strive for a future where technology serves as an ally, enhancing human experiences while safeguarding the well-being of users and society. Together, we can shape a user-centred world that is both delightful and morally sound.
Image by:
Daria Nepriakhina from https://pixabay.com//
References:
Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20. https://doi.org/10.1038/538020a
Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V., & Zou, J. (2021). Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review. JAMA Dermatology, 157(11), 1362–1369. https://doi.org/10.1001/jamadermatol.2021.3129
Hermann, E. (2022). Psychological targeting: Nudge or boost to foster mindful and sustainable consumption? AI & SOCIETY. https://doi.org/10.1007/s00146-022-01403-4
Kara. (2022, August 19). McKinsey and Its Opioids Scandal. Seven Pillars Institute. https://sevenpillarsinstitute.org/mckinsey-and-its-opioids-scandal/
Marshall, P. (2022). Scandal at the Post Office: The Intersection of Law, Ethics and Politics. Digital Evidence and Electronic Signature Law Review, 19, 12.
Möhlmann, M. (2021). Algorithmic Nudges Don’t Have to Be Unethical. Harvard Business Review.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469–481. https://doi.org/10.1145/3351095.3372828
Rilling, J. K., & Sanfey, A. G. (2011). The Neuroscience of Social Decision-Making. Annual Review of Psychology, 62(1), 23–48. https://doi.org/10.1146/annurev.psych.121208.131647
Schmauder, C., Karpus, J., Moll, M., Bahrami, B., & Deroy, O. (2023). Algorithmic Nudging: The Need for an Interdisciplinary Oversight. Topoi. https://doi.org/10.1007/s11245-023-09907-4
Wang, P., & Johnson, C. (2018). Cybersecurity Incident Handling: A Case Study Of The Equifax Data Breach. Issues In Information Systems. https://doi.org/10.48009/3_iis_2018_150-159
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