In my home, I am the proud owner of not just one, but four virtual assistants. Without context, you’d probably think Alexa was a close family member, and considering the amount of times I have to tell her to turn the music down before she complies, she really is starting to resemble my younger sister. Like the crux of many a science fiction plot, the artificial eavesdroppers that filter through my everyday happenings make me question whether robots truly will take over. As we continue to experience a world where our sleep patterns are tracked by the watches on our wrists and tailored ads make us question whether our phones really are listening, it would be fair to say we’re already living that reality.
As AI infiltrates our routines, it is perhaps natural as a designer to start considering the impact this has on our practice. Up until recently, there has been a reasonably stark distinction between the human-dependent design process and machine learning. Yet, as the capabilities of AI extend and the nature of algorithms and their usage become increasingly more complex, the boundaries in this distinction become blurry. Where AI has the potential to revolutionise our design practices and the consequent lives we lead, it simultaneously forces us to question, engage and challenge both its influence and our own.
Design and designers, by nature, aren’t utilitarian. Neither lend themselves to the level of pragmatism and mathematical logic we are quick to associate with the machine robots of our world. As a result, when other industries wonder if AI will eventually replace their workforce, there is a certain security in being a designer, where our levels of empathy and genuine understanding has value beyond coded 1’s and 0’s. Being designers, we absorb so much beyond that which algorithms can deduce. Our own deductions are weighted by feeling and a social intelligence that we like to think could never be translated into code.
As a designer, my own practice is directed by my ever-growing interest in the human psyche. The visuals, narratives and strategy I produce are all funnelled through empathetic reasoning and reference to behavioural economics. There are great nuances within the moral and ethical landscapes of the world, and it is within the role of the designer to capture such subtleties in an effort to make work that resonates. So what happens when we start putting machine learning into the equation? If AI develops to a point where it understands human complexity at the same level we as designers pride ourselves on, albeit an understanding rooted in figures and stats, does the empathy gap become more of a marginal difference? If so, are we as designers fated to take on more of a curatorial role, rather than a creative one? The whole thing sounds increasingly like a Black Mirror episode.
If it wasn’t feeling dystopian enough, the growth of AI and its an underlying presence within our daily lives is leading to disparities, misconduct and outright discrimination. Despite their presumed objectivity, machines are quickly becoming a central force in facilitating the human tendency to create hierarchies and subsequent divisions. As stated by Joy Buolamwini in her TED talk, the nature of algorithmic bias spreads like a virus (something we can all relate to), leading to ‘exclusionary experiences’ and ‘discriminatory practices’. So where do designers come in? Well, despite the aforementioned empathy gap narrowing between ourselves and the machines, it’s important to understand that a balance can be achieved and, in turn, create environments where AI can be used in an effective yet controlled manner. Our understanding as designers of moral and ethical behaviour and our sensitivity to humanity make us important players in the expansion of AI and the nuances of algorithms. As designers, we are at the epicentre of global and social change, and as suggested by Buolamwini, we have a role to play in the creation of inclusive code and a consequent just society.
Buolamwini, J (2016) How I'm fighting bias in algorithms Available at: https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms/transcript?language=en (Accessed:5 April 2021).
NY Times 2021. Biased Algorithms Are Easier to Fix Than Biased People (Published 2019). [online] Available at: <https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html> [Accessed 5 April 2021]