As the technology landscape broadens, so do technology uses. Staying up to date with the latest and greatest, people are continuously inventing and finding new ways to utilize technology to enhance personal convenience or improve task efficiency. These goals are often the same for large businesses looking for ways to improve productivity or drive new innovation. A great way to achieve these goals is by leveraging artificial Intelligence (AI) and Machine Learning (ML).
Artificial Intelligence provides organizations with the opportunity to get accurate results almost instantly, a rate of speed that is unmatched without such technology. According to an IDC report, “Global spending on artificial intelligence (AI) is forecast to double over the next four years, growing from $50.1 billion in 2020 to more than $110 billion in 2024.” This statistic proves that AI adoption is on the rise, but that also allows room for data ethics issues to arise within the industry.
A leading data ethics question is: How can humans trust the outcome of a machine’s AI or ML algorithm without understanding the process of how or why it came to its given decision? This question has formed the basis for the AI Explainability topic within the data ethics and machine learning space. In a Forbes article, Understanding Explainable AI, author Ron Schmelzer describes how “as humans, we must be able to fully understand how decisions are being made so that we can trust the decisions of AI systems. The lack of explainability and trust hampers our ability to fully trust AI systems.”
There must be transparency in a machine’s data model to build trust with its users. Data engineers want to promote a trustworthy relationship between humans and AI, and they are doing that in various ways. Dell Technologies surveyed IT decision-makers, and 52% reported “they would take steps to improve data traceability and expose bias in order to promote trust in AI algorithms and AI decision-making. 49% are seeking to build in more fail-safes, and 44% would advocate for sensible regulation.”
To improve transparency of AI models, organizations need to understand and control the data that is being fed into the models. One way to improve this transparency is through DataOps which provides a holistic approach to data management. DataOps connects the end-to-end data supply chain to improve visibility and traceability, providing users with a birds-eye view of their entire data landscape along with a data governance framework required to support explainable AI.
By leveraging a DataOps approach, companies can ensure data quality, track data lineage, control data access, and define and enforce governance policies around data usage for AI models.
Companies that integrate a DataOps approach can set a real example for those within the AI community, establishing themselves as a building block in the AI explainability narrative alongside developing the data management and governance strategy to sustain the growing adoption of AI within any industry. It will take time and continued effort from IT decision-makers to educate users and regulate AI systems to reach an ideal level of trust. Are you ready to take on AI? Set up a custom demo with our data experts to see how Zaloni’s Arena DataOps platform can help improve AI outcomes and enable AI explainability.
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