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The Future of
Marketing & AI?
When it comes to
specific technologies like
AI, we support accountability through publicly
available commitments like the model cards
mentioned earlier and our Generative AI
guidelines for responsible development.
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Generative AI & Responsibility
Pairing the power of technology with Salesforce’s passion for trust, our principles for responsible
generative AI development aim to keep our values at the core of our approach to innovation.
Accuracy:
We need to deliver verifiable results
that balance accuracy, precision, and recall in the
models by enabling customers to train models on
their own data. We should
communicate when there
is uncertainty about the veracity of the AI’s response
and enable users to validate these responses. This
can be done by citing sources, explainability of why
the AI gave the responses it did (e.g., chain-of-thought
prompts), highlighting areas to double-check (e.g.,
statistics, recommendations, dates), and creating
guardrails that prevent
some tasks from being fully
automated (e.g., launch code into a production
environment without a human review).
Safety:
As with all of our AI models, we should
make every effort to mitigate bias, toxicity, and
harmful output by conducting bias, explainability,
and robustness assessments, and red teaming.
We must also protect the
privacy of any personally
identifying information (PII) present in data used for
training and create guardrails to prevent additional
harm (e.g., force publishing code to a sandbox rather
than automatically pushing to production).
