How To Eliminate Ethical Bias From Machine Learning And Data Analytics

Julien Alteirac, Regional Vice President of UK&I at Snowflake explores…
 
Over the next few years the increasing availability of tools such as AutoML will help democratise machine learning (ML) and empower businesses to tap into near real-time data. Organisations will enjoy the benefits of automation more cost-effectively, without needing to rely on as many specialised data scientists.

However, for all the promise the likes of AutoML holds, organisations must remain acutely aware of eliminating any potential biases encoded in ML algorithms, and encourage an ethical data science environment to ensure effective and accurate data insights. Tackling such bias requires companies to build a team that can look at not only the algorithms, but also the data, conclusions, and results, in an equitable and fair-minded way.
 

Use representative data

 
Structurally, data can be biased because if it doesn’t accurately represent a model’s use case when being analysed by a machine learning algorithm, it will produce skewed results. When examining the risk of bias in ML, companies must first ask themselves, are we using a broad enough set of data that we’re not presupposing the outcome? If the answer is, no, then IT and data teams should be widening their net to ensure all relevant data captured is representing a comprehensive cross-section of the entire business to provide the most equitable results.

Additionally, organisations can leverage third-party data from data marketplaces, enabling them to build more sophisticated AutoML models. This is because they are powering algorithms with wider, more unique datasets from the wider marketplace, reducing the risk of bias within the models themselves. The development of successful AutoML models will also see organisations sharing and monetising these, as part of a more collaborative data sharing ecosystem.
 

 

Eliminate coded bias in algorithms

 
Once a broad, diverse set of data has been established, companies must then confront the issue of potential bias in the algorithm itself. How an algorithm is coded depends on the actions and thought process of the person doing the coding, meaning that it is susceptible to bias depending on who actually wrote it.

This is why business leaders should consider the impact that diversity in the workforce has on ML algorithms and build a team that can look at data in a fair and equitable way. To create such a team, organisations need to consider all dimensions of diversity including experience, socio-economic background, ethnicity and gender. It’s not just one factor. It’s multidimensional like so many things in analytics. Diversifying the workforce and establishing a dedicated team whose responsibility it is to resolve issues of bias is a significant step towards ethical ML and data analytics.
 

Building the foundations for a diverse future

 
If companies are serious about eliminating potential bias in ML algorithms, they must create tangible actions that will help bring about ethical practices. This will take a multi-layered approach by broadening their datasets and diversifying their workforce to remove coded bias in algorithms. Building an ethical data science environment depends on such actions and will help create the foundations for a future of diverse, equitable and accurate data insights.