The world of tech often speaks of creating a new, better world — but we cannot do that if the solutions we build propagate pain, punish the vulnerable, and create exponential imbalances in resources, power, and opportunity through the exclusion of diverse communities.
Let’s face it! We all have biases – and the technology we produce often scales our prejudice, particularly when there’s little or no diverse representation. This contributes to a partisan, racist world. Today, tech companies are incorporating AI-based technologies into their businesses at an unprecedented rate. Machine learning algorithms are in everything from digital transformation and healthcare to criminal prediction, job matching, cybersecurity, and more. Data labeling is done to fit the mainstream. No one calls out irregularities because they don’t recognize them.
Diversity delivers value. As more people stand in solidarity to drive positive change, we are finally acknowledging the fact that our common humanity does not mean we share the same reality. Real representation matters because what we do (or don’t do!) in tech hurts people at scale.
To prepare for the next phase of AI, leaders must prioritize not just technology infrastructure, but the right mix of people informing the data.
Many executives running global companies are starting to recognize that diversity needs to factor in data annotation, tagging, and classification. However, there’s a persistent gap: Training data rarely reflects the global nature of users.
So how do we fill this gap?
We have an amazing global team of content creators, copywriters, data scientists, and devs who are driven to help you get more done, particularly during this time of digital transformation.
We have a full-time product and engineering team dedicated to continuously improving how we work in order to serve our customers’ dynamic industries. Reactionpower’s proprietary training data annotation platform helps facilitate large data projects through the customization of task workflows, task distribution, multi-tiered quality control, and project-based training.
Reactionpower has three main functions: task distribution, data structuring work (image annotation, data categorizing, other writing/research), and quality management. Our work distribution system allows a large team of data annotators to work simultaneously on one project’s queue of tasks, securely. Our data structuring work allows data annotators to perform custom work, such as annotating images with specific labels. Our quality management system allows quality managers to check the quality of work and provide feedback to annotators to ensure teams are working efficiently and effectively.
At Reactionpower we focus on 3 core foundational activities to ensure that the data is high quality and leads to useful insights:
1. Better data governance
Very few businesses have formal activities in data quality assurance, which points to the need for a greater commitment to data governance in support of advanced analytics. At Reactionpower we explore various approaches to data architecture, governance, and quality that can build trust in data for analytics.
2. Data privacy
Data security is one of the strongest reasons why businesses work with us repeatedly, but there are opportunities to increase the maturity of security practices by applying analytics and AI in this arena. We are consistently evaluating opportunities to approach privacy and CDPR as a way to build trust with customers, rather than simply treating them as compliance-driven mandates.
Data drives innovation. Our company supports a culture of both creativity and analytics-driven innovation. We are consistently doing more to spread the necessary skills and mindset throughout our organization as well as educating, communicating, and collaborating with customers.
Over the years we’ve found that communication and close collaboration between data and business experts lead to mutual understanding and measurable benefits. We are committed to continuous growth and innovation as we serve our customers.