Julien Alteirac, Regional Vice President of UK&I at Snowflake, explores…
Over the next few years, the increasing availability of tools like AutoML will help democratize machine learning (ML) and enable companies to mine data in near real time. Organizations will reap the benefits of automation more cost-effectively without having to rely on so many specialized data scientists.
However, for all the promise that AutoML promises, companies must be mindful of eliminating potential biases encoded in ML algorithms and fostering an ethical data science environment to ensure effective and accurate data insights. To address such biases, companies need to build a team that can look not only at the algorithms but also at the data, conclusions and results in an equitable and fair manner.
Use representative data
Data can be structurally biased because if it does not accurately represent a model’s use case when analyzed by a machine learning algorithm, it will produce skewed results. When examining the risk of bias in ML, organizations must first ask themselves: are we using a sufficiently broad data set not to assume the outcome? If the answer is no, IT and data teams should stretch their net to ensure that all relevant data collected represents a comprehensive cross-section of the entire organization for the most equitable results.
In addition, companies can leverage third-party data from data marketplaces to build more sophisticated AutoML models. This is because they feed algorithms with broader, more unique datasets from the broader market, reducing the risk of bias within the models themselves. The development of successful AutoML models will also result in organizations sharing and monetizing them as part of a more collaborative ecosystem for data sharing.
Eliminate coded biases in algorithms
Once a broad, diverse dataset has been created, companies must address the issue of potential bias in the algorithm itself. How an algorithm is coded depends on the actions and thought processes of the person doing the coding, which means it’s susceptible to bias depending on who actually wrote it.
For this reason, business leaders should consider the impact that workforce diversity has on ML algorithms and build a team that can look at data in a fair and equitable way. To assemble such a team, organizations must consider all dimensions of diversity, including experience, socioeconomic 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 solve problems with bias is a significant step towards ethical ML and data analytics.
Laying the foundations for a diverse future
If companies are serious about removing potential biases in ML algorithms, they need to take concrete actions that help establish ethical practices. This will take a multifaceted approach, expanding their datasets and diversifying their workforce to eliminate coded biases in algorithms. Building an ethical data science environment depends on such actions and will help lay the foundations for a future of diverse, equitable and accurate data insights.
https://techround.co.uk/news/how-to-eliminate-ethical-bias-from-machine-learning-and-data-analytics/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-eliminate-ethical-bias-from-machine-learning-and-data-analytics How to eliminate ethical biases through machine learning and data analysis