Report shows security and privacy blind spots in AI development

More than half (59%) of organisations also don’t consider fairness, bias or ethical issues during ML development. Privacy is similarly neglected, with only 35% checking for issues during model building and deployment.

Instead, the majority of developmental resources are focused on ensuring AI projects are accurate and successful. The majority (55%) of developers mitigate against unexpected outcomes or predictions, but this still leaves a large number who don’t. Furthermore, 16% of respondents don’t check for any risks at all during development.

This lack of due diligence is likely due to numerous internal challenges and factors, but the greatest roadblock hindering progress is cultural resistance, as indicated by 23% of respondents.

The research also shows 19% of organisations struggle to adopt AI due to a lack of data and data quality issues, as well as the absence of necessary skills for development. The most chronic skills shortages by far were centred around ML modelling and data science (57%). To make progress in the areas of security, privacy and ethics, organisations urgently need to address these talent shortages.

“AI maturity and usage has grown exponentially in the last year. However, considerable hurdles remain that keep it from reaching critical mass,” said Ben Lorica, Chief Data Scientist, O’Reilly. 

“As AI and ML become increasingly automated, it’s paramount organisations invest the necessary time and resources to get security and ethics right. To do this, enterprises need the right talent and the best data. Closing the skills gap and taking another look at data quality should be their top priorities in the coming year.”

Other key findings include:

• The overwhelming majority of organisations (81%) have started down the route of AI adoption. Most are in the evaluation or proof of concept stage (54%), while 27% have revenue-bearing AI projects in production. 
• A significant minority (19%) of companies have not started any AI projects.
• Machine learning has emerged as the most popular form of AI used by enterprises. Nearly two-thirds (63%) use supervised learning solutions while 55% are using deep learning technology. Model-based methods are used by almost half (48%) of respondents.
• AI is most likely to be used in research and development (R&D) departments (50%), customer service (34%) and IT (33%). Legal functions have seen the least innovation, with only 5% making use of AI technologies.
• TensorFlow (55%) and scikit-learn (48%) are the most popular AI tools in use today.

The research took the form of a survey which received more than 1,300 responses from senior business leaders. The majority of respondents worked across three major industry groups - technology, financial services and healthcare and life sciences.