Over the past few years, inspired by the promise of Artificial Intelligence (AI), we have seen enterprises embrace the first big challenge of AI: building it in the first place. There has been significant adoption of machine learning (ML) and AI in enterprises, aided by the broad availability of solutions for data preparation, model development and training, and model deployment. Now, however, we are seeing enterprises shift their focus from getting these basic building blocks in place to tackling the next big challenge: how do you drive real, sustainable business value with AI? Answering this question requires solving a whole new set of problems. It requires solving the challenge of AI Quality. At TruEra, we believe that solving the problem of AI Quality is key to driving and preserving business value.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. With artificial intelligence (AI) and machine learning (ML) now serving as key attributes to make IT systems faster, more accurate and beneficial for an enterprise's bottom line, the importance of transparency in how these components are working also becomes more critical . Why? Biases can creep into AI / ML models just as it does in humans, and when it does, queries can go awry and skewed analytics can cause production results to be incorrect. Explainable AI is important for trust, compliance and building less-biased AI models. Both customers and regulators want to know more about what's inside the black box.

TruEra has made its tool for explainability in machine learning models … Explanations for Deep Convolutional Networks' by the creators of Carnegie …

Automated decision-making tools are becoming increasingly ubiquitous in our world. As ML models become more widely adopted, special care and expertise are needed to ensure that artificial intelligence (AI) improves the bottom line fairly. ML models should target and eliminate biases rather than exacerbate discrimination. But in order to build fair AI models, we must first build better methods to identify the root causes of bias in AI. We must understand how a biased AI model learns a biased relationship between its inputs and outputs.

Truera, a startup developing a machine learning model intelligence platform, today announced it has raised $12 million. The company says it will put the funds toward product R&D and expanding its geographic footprint. According to a McKinsey survey, while 39% of executives say their companies recognize the risks associated with lack of "explainability," or the ability to explain how AI models come to their decisions, only 21% say they're taking steps to actively address the issue. But a separate report implies that "trustworthy AI" is fast becoming a business imperative. An IDC report found that business decision-makers believe fairness, explainability, robustness, data lineage, and transparency -- including disclosures -- are "critical requirements" in AI that need to be addressed now. Truera's technology builds on six years of AI explainability research undertaken at Carnegie Mellon University.

Financial and banking services company Standard Chartered turned to a model intelligence platform to get a clearer picture of how its algorithms make decisions on customer data. How machine learning comes to conclusions and produces results can be a bit mysterious, even to the teams that develop the algorithms that drive them -- the so-called black box problem. Standard Chartered chose Truera to help it lift away some of the obscurity and potential biases that might affect results from its ML models. "Data scientists don't directly build the models," says Will Uppington, CEO and co-founder of Truera. "The machine learning algorithm is the direct builder of the model."

Financial and banking services company Standard Chartered turned to a model intelligence platform to get a clearer picture of how its algorithms make decisions on customer data. How machine learning comes to conclusions and produces results can be a bit mysterious, even to the teams that develop the algorithms that drive them -- the so-called black box problem. Standard Chartered chose Truera to help it lift away some of the obscurity and potential biases that might affect results from its ML models. "Data scientists don't directly build the models," says Will Uppington, CEO and co-founder of Truera. "The machine learning algorithm is the direct builder of the model."