Justifai’s privacy-preserving AI suite enables you to personalize experiences while respecting privacy and minimizing compliance risk.

Why you need a privacy preserving personalization AI suite

Challenges for persomalizing high-risk decisions

Customers are accustomed to personalized, anytime, anywhere retail, entertainment and education experiences offered by Amazon, Netflix and alike. Now, customers also expect more personalized, anytime, anywhere experiences in other sectors such as the health care & life sciences and insurance.

Consequently, all companies have to switch from a reactive to proactive business model and from offering a limited number of population-based experiences to a multitude of hyper-personalized experiences.

Responding to this personalization trend is harder if your company is in a sector with high-stakes decision making, sensitive data, and that is highly regulated. How can your company safely use this personal and potentially even sensitive data to personalize experiences while minimizing compliance risks? 

That’s why you need a privacy-preserving AI suite such as Justifai.

Justifai's 5 Core Pillars

AI Compliance

Compliance Gate Keeper

Develop and roll out compliant digital solutions faster, more efficiently and at reduced cost, even when using sensitive data.

Automatically stay compliant even when scaling into different geographies or when regulation changes.

AI Ethics

Ethics Gate Keeper

Build digital solutions with ethics safeguards that respect your values by design.

Reduce reputational risks and foster user trust and adoption by automatically building AI solutions meeting the Ethics Guidelines or Ethics standards of your choice: e.g. ALTAI , RAI framework, IEEE Ethical Aligned Design, NIST AI RMF 1.0, AI Verify, etc. 

trustworthy AI for business users

Trust based personal data collection

Create user/patient trust by offering full transparency of the purpose & value of requested data.

Increase user engagement and adherence. Enhance the success rate of your digital platform and health outcome objectives.

Increase and improve total RWE/RWD data collection over time.

Privacy-preserving machine learning

Privacy-preserving machine learning

Lower churn rate due to higher user engagement and  better personalized app experience from day 1, even with limited user information.

Hyperpersonalized content/intervention recommendations while respecting varying privacy & sharing preferences and protecting compliance and ethics.

Brand reputation: Higher user trust due to minimal use of data by design and full transparency on purpose and value of data requested.

AI Audit

Privacy-aware auditability

Reproducibility: Capture, document and explain every decision across the entire AI solution.

Transparency: Clearly document & prove compliance to regulations. 

Explainability: Tailor the explainability & audit content to the access rights of the recipient


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