59% of organizations have already published metrics for the development and governance of ethical models and Artificial Intelligence within them. However, 73% of respondents consider it a challenge for management to prioritize responsible AI and ethical practices, as 67% expressed that their company's board of directors has no knowledge of AI.
One way that Fico suggests you help your client maintain ethics in Artificial Intelligence is by monitoring the models, as 50% of the corporations surveyed do.
You can also identify latent features of the model to monitor for AI ethics, as 45% of respondents do, or replicate what the stats-based 43% do to uncover potential changes in production data.
For many companies, Artificial Intelligence is a key entity in business strategy, although for only 20% it is already relevant to take care of ethics. Another 30% say that this issue will be considered in the next three or five years; 32% between one and two years, and only for 15% the following 12 months will be a priority.
Artificial Intelligence, security and compliance
All AI models have some potential for misuse. The study points out that the five most risky models are Natural Language Processing; Speech recognition; Synthetic Data; Decision Trees and Learning Rules by Association.
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To combat security biases, 60% of organizations evaluate the impartiality of the results; 55% isolate, evaluate the latent model and test to identify biases; 50% detect bias in unstructured data.
The model with the least risk so far is the Artificial Neural Network.
Companies that invest in Artificial Intelligence are aware of the importance of securing it against malicious attacks, since at least 30% have suffered attacks on their AI in the last twelve months.
To protect your client from AI you can build elements of randomness into AI models, for example, stochastic defense, as 65% of respondents did; emphasizing the review process and security policy in design like 50% of respondents; or how the 28% did by controlling and overriding the real world data before it is used.
Now, when it comes to compliance, only 7% of respondents describe their processes for regulatory compliance as very effective, with 46% saying their approach is very inefficient.
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