The Privilege Associated with Risk Appetites
What is your risk appetite? More importantly, what determines your risk appetite? You hear incredible stories from (typically) white men who have succeeded in the venture capital space or with tech startups. These men emphasize their high-risk tolerance as a crucial part of their success, which makes sense. To them.
We get grilled by ‘high risk = high reward’, whether it is when we are investing in stocks, personal relationships, or career choices. Except not everyone can tolerate high risk, and not because they do not want to, but simply because they cannot. If you are living paycheck to paycheck or have limited savings, you are not going to accept an apprenticeship that will not pay you. You are not going to invest in a risky stock that could lose money. You are not going to work on a tech startup which has a dicey 90% failure rate.[1] It is simply too risky for you. This barrier to entry to a very lucrative space (venture capital expects returns between 25–35% from an investment)[2] perpetuates inequality and continues to marginalize communities. Minorities and marginalized communities are in an even tougher position because they do not have the same access to mentors and connections that can help white men succeed.
It is easy to talk about components of success when you do not have these barriers to entry. This bias in the industry permeates different aspects of venture capital and tech. Just look at Amazon’s assisted recruiting Artificial Intelligence (AI) program, Microsoft’s Xbox gaming system, or facial recognition systems that best identify white men. The output represents the culture that created it. That should not surprise us. To quote Sandra G. Mayson, “bias in, bias out.”
So now, not only are minorities and marginalized communities barred out of the space, but the tech that was promised to be less biased due to limited human involvement, is now working against them. And instead of fighting hard to find solutions for these biases, companies are terminating these projects and resorting back to the practices that created the biased data that these systems were trained on. Let me emphasize that this is not a solution. If the output is biased, then the practices and cultures are biased as well. Reverting to them does not solve the problem, it avoids it.
If you are part of a marginalized community, I am sure this is frustrating to read. I assure you, typing this is not easy, but we need to hope for a better future, and we should work for one. Talking about these issues is the first step. We need to raise awareness. Some people in power are unaware of their own biases. Many may mean well but come off as tone deaf when addressing these issues. Thus, we need to find a new way to have difficult conversations. One idea may focus on communication methods. Instead of lecturing people on their success, those in power should start conversations. I have read so many books written by successful white men and listened to lectures by them, but I have never been invited into the conversation. That is a problem. Minorities and marginalized communities need to be part of the discussion if we want to find solutions to the obstacles we face and to de-bias data. Collectively, we need to learn how to communicate to ensure that all voices are heard and respected and to have a meaningful impact on the environments creating the data.
Communication on its own is not enough. We need acceptance. Repeat after me: The venture capital space has huge barriers to entry for minorities and marginalized communities. The data we use to train tech models is biased. Whether it is biased against women, people of color, other minorities, or the intersectionality of many of these marginalized communities, the fact remains: the bias is overwhelming. We need to recognize our privilege if we are in the space and if we are creating the data, and we should act to support others.
There is no easy solution to this and whatever path we embark on will be a long one. Results will take time and people will continue to question the bias embedded in the data and the culture but reverting to old solutions that created the bias in the first place is not the answer. We need to continue working on changing the cultures and environments that perpetuate bias. We need to put humans first. We need human centered design. You cannot escape the human element and our input, even in tech.
[1] Bryant, Sean. “How Many Startups Fail and Why?” Investopedia, Investopedia, 30 May 2021, https://www.investopedia.com/articles/personal-finance/040915/how-many-startups-fail-and-why.asp#:~:text=The%20Small%20Business%20Administration%20(SBA,70%25%20in%20their%2010th%20year.
[2] Zider, Bob. “How Venture Capital Works.” Harvard Business Review, 20 Dec. 2021, https://hbr.org/1998/11/how-venture-capital-works#:~:text=They%20expect%20a%20return%20of,have%20a%20lot%20of%20latitude.
