Introduction to Data Science Tools Resources in the We Count Research Library

By Lorna Lo

The issues with bias, gaps and fairness in AI can be addressed by ensuring persons with disabilities can equally participate in the field of data science. The tools used in data science are numerous. They differ greatly in their purpose, interface, complexity and capabilities. Unfortunately for data scientists with a disability, use of these tools can be challenging and unpredictable as their accessibility can vary greatly. There are often shortfalls in the tools’ designed accessibility, with accessibility features included as an afterthought. Persons with disabilities are left to develop workarounds and use trial-and-error methods to determine how assistive technologies can be used effectively with data science tools. The result is a patchwork of solutions and strategies that are less than ideal yet require constant adjustments.

Learning data science comes with a steep learning curve. Inaccessible tutorials, learning environments and data science resources create barriers; and the need for users to investigate the idiosyncrasies of each data tool and their interaction with assistive technologies only creates additional obstacles. Users not only have to be persistent and extremely resourceful in finding solutions, but they must also make difficult decisions on whether it is worthwhile to invest valuable time in learning the newest popular tool only to discover that accessibility shortcomings render it unusable.

Despite these challenges and hurdles, persons with disabilities are working in data science. To support a more inclusive environment in the field of data science, We Count’s resource library provides:

  • Information about data science tool accessibility and compatibility with assistive technologies,
  • Experiences and resources from persons with disabilities working in data science,
  • Accessibility strategies for specific data science tools and data science techniques, and
  • Resources to network with others working in data science.

We hope this library provides a consolidated resource that can continue to grow as data scientists use and add to this knowledge base. We have already seen how shared experiences between data scientists with and without disabilities can create positive changes in data science tools. It is vital that we continue to advocate for accessibility in these tools by providing ongoing feedback during the development, use and application of these tools. Only when data science tools can be used by all will AI be inclusive for all.

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