Q-Bias? A-Teamwork

Issues of bias and accessibility can crop anywhere unknowingly and can have severe consequences. The important thing to take into consideration is the fine distinction between preferences and bias. Although both might originate from upbringing, past experience, or lack of information, biases tend to be unfair in nature, and cause partiality and discrimination, while preferences are harmful and merely a liking of one state of things over the other.

In our topic “Agriculture Intelligence” dealing with urban farming, the domain of bias and accessibility is very limited. Few areas where they may arise can be regarding plant data that favor one type over the other, or well-documented farming practices over superior indigenous practices that haven't been documented well enough.

Another area where we would need to be careful would be the preference of commercial/money-making crops over best suitable crop for a given condition. But to put this category in bias would be going a bit far, as individual needs and purpose also come into play.

These kinds of biases described above should not, in theory, have any negative consequences, and can be continuously removed with ingesting more data as required.

During research, my personal biases might affect user research and analysis of data, which can substantially affect the product direction. These might be in relation to the user groups we interview, data we might give more importance to over other, the analysis of one set of answers over other, and gravitating too early towards solutions based on personal preferences and what I think the problem-solution pair is.

The best way I feel to overcome bias is the team. Other team members, their opinions, and a healthy critique of ideas can be a strong and effective tool in removing biases from an early stage. Each member brings a different perspective, ideas, and experiences, and these can be really helpful in differentiating preference from the process. Using blank research templates and design tools suggested by design foundations( IBM, IDEO, Bloomberg Foundation, Frog, MIT) can also help counter unconscious biases arising from lack of adherence to a design process. These tools can help conduct research, sort data, and guide analysis in a comprehensive and objective way, allowing us to make conscious, data-driven decisions over personal preferences.

Accessibility issues may arise from our product requiring a pre-requisite knowledge to use, or expensive tools to be integrated, but these would majorly be dependent on available technology and method of intervention in the chosen field.

The topic of bias and accessibility is such that to consciously think about and remove all of them before-hand is a difficult task. As we go through the design journey, we would encounter many more biases which we can’t think of right now. The best way to address those as they come is to be willing to accept and resolve them, keep an open mind, and use the team and the tools to make positive progress towards the best solution, not “your” solution.