Today, Thoughtworks, a global software consultancy, released Volume 21 of Technology Radar, a bi-annual report based upon Thoughtworks’ observations, conversations and frontline experiences of solving its clients’ toughest business challenges. The latest edition highlights how emerging tools such as What-If and techniques such as ethical bias testing make machine learning (ML) more intelligible, why software development should be regarded as a team sport, how to navigate the increasingly competitive cloud marketplace and the evolution towards governance as code.
“One of the most important topics on this year’s Technology Radar is machine learning explainability,” says Dr. Rebecca Parsons, chief technology officer at Thoughtworks.
“Machine learning tools are used to make life-impacting decisions, however many of these models are inherently opaque. This is problematic when people need to know how a decision was made. Similarly, if the training process isn’t open, there’s a risk of introducing prejudice, sampling, algorithmic or other bias into a machine learning model.”
To combat issues with machine learning and explainability, Thoughtworkers urge business leaders and IT managers who oversee machine learning ecosystems to increase the persity of their development teams to reduce unintentional risks in ML models, and to use tools that can reduce algorithmic bias.
Other noteworthy themes included in the Technology Radar Vol. 21 include:
Thoughtworks’ Technology Radar is released twice a year, but Thoughtworks encourages other companies to apply Radar-like thinking year-round to critique their own technology landscape.
“By discussing the blips for our Technology Radar, we identified a most precious value: knowledge (about what works well and what attempts were in vain),” says Thomas Spillecke, IT architect of cloud applications at Porsche. “The Technology Radar preserves our knowledge — but only works if we update it regularly.”
Visit Thoughtworks.com/radar to explore the interactive version or download the PDF version.