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On the face of it, bias in cold hard data might seem like a paradox, but delving a little deeper reveals that it’s far more prominent than many people think.
Systemic bias, selection bias, confirmation bias, automation bias, even bias AI, all contribute toward bad data study designs, poor collection methodology and ultimately, a severe lack of representation.
In the life sciences space, data bias has the potential to be deadly. In recent years, the developments in AI and machine learning have had a major part to play in clinical decision making, and while extraordinary progress has been made, a fresh set of ethical challenges need to be addressed.
Informing Clinical Decisions
If the bias inhibits the data used to inform clinical decisions, medical solutions will likely not represent the needs of the population. For example, some diseases manifest differently among certain groups of people, (Nature, 2019) and these differences must be accounted for in the clinical development process, otherwise, treatment will lack the precision required to remain effective.
Bias data could lead to misdiagnosis for entire groups within the population, resulting in dire consequences.
It’s not a new concept by any means, but as more data is generated and AI becomes more prevalent throughout the life sciences, the challenge of beating back data bias is greatly exacerbated.
Combatting Bias in Data
The recruitment industry is uniquely positioned make a positive difference in beating data bias, provided it can remove said bias from the hiring stage and build more representative workforces.
From graduate machine learning engineer jobs to biostatistics director roles, any life sciences recruiter worth their salt will be able to tell you that underrepresentation is an issue that effects outcomes in the life sciences, regardless of the seniority level it's witnessed at.
Bias AI is representative of the teams that built the systems in the first place. If these teams are more diverse, the very real threat of human bias can potentially be mitigated.
When different perspectives and experiences are implemented into the algorithms used to make clinical decisions, inclusivity has the chance to thrive, directly influencing both the outcome of the process, and the health and wellbeing of the public.
Using a diversity led hiring methodology, specialist recruiters have the opportunity to identify gaps in the market and help build more impactful data science teams, resulting in a higher quality of final product.
Alongside the wider-reaching positive impact, better products and services directly impact the bottom line for increased profitability.
The Future of Data
As AI and big data continue along their staggering rate of evolution and implementation, the need to hire diverse teams will only become stronger.
Comprehensive policies will need to be established and adhered to in the fight against data bias, and (somewhat ironically) purpose-built algorithms will continue to have a large role to play in explaining the existence of bias in future outcomes.
Constant process and system evaluation is critical to success in the life sciences, not just in terms of data bias detection, but in every aspect of the space, be it regulatory functionality or engineering.
At BioTalent, we have a wealth of experience in hiring for the life sciences, a passion for people, and a mission to make a positive impact on the wider world.
We take a knowledge-based approach to hiring in the data field, and it’s underpinned by our DEIB-led methodology.
If you need some support with your own hiring goals, or you have any questions at all about our process, reach out to our team today, we’re here to help.