Responsible AI: The Blend360 Way

Blend360 is committed to the responsible use of Artificial Intelligence across our organization. We recognize the remarkable power to drive competitive advantage and improve productivity, while acknowledging the significant ethical and societal implications it presents.

Our clients trust us to manage with a strong ethical foundation across all aspects of our business, and AI is no exception. We adhere to the highest standards of transparency, fairness, privacy, accountability, and social and economic benefit.


Blend360 is committed to delivering AI that is based on a strong set of principles that ensure safe and effective use.


Beneficial: Our AI systems support human goals and keep humans at the center of the experience.
We consider both economic and ethical factors to benefit people and society at large.
We maximize their benefits while addressing any potential harms.
We mitigate potential negative impacts, such as job displacement or the amplification of bias and other harmful content.


Transparency helps our stakeholders make informed decisions about using our AI systems, ultimately building trust and confidence. We believe that:
Clear and consistent documentation using data and model cards provides the visibility to understand the proper uses of the system.
AI should operate transparently, with clear explanations about how decisions are made, the algorithms used, and the system’s limitations.


Along with transparency, fairness in AI systems mitigates the potential for individuals or groups to be systematically disadvantaged. For this, we strive to:
Minimize biases in our AI systems by rigorously testing our models to uncover and address any unintended biases.
Ensure that the data used to train these models is complete, accurate, and representative of the diversity of the user base.
Contribute to social justice and not exacerbate existing inequalities.


Blend360 respects the privacy of our clients, their users, and our stakeholders. 
We comply with best practices and legal requirements for anonymizing, encrypting, and safeguarding all data we use.
We never use the data provided by our clients for any purpose other than those expressly agreed upon.


Secure design protects the interests of organizations by protecting them from misuse. We strive to:
Include safety principles to ensure respect for privacy and the people interacting with our systems.
Protect both the AI system and the underlying data.


AI Systems must allow for feedback so that developers can address potential bias or other issues. For this, we:
Deployed systems provide a channel for groups or individuals to voice concerns if they feel unfairly impacted by AI systems.


Blend360 builds responsible AI throughout the lifecycle of all client engagements to ensure fairness, privacy, security, and overall accountability.

Planning and Model Design:

Consider and document the intended use.

  • Establish the ethical guidelines to which the model will adhere.
  • Establish intended goals around principles like fairness, privacy, security, and transparency.
  • Clarify limitations/risks associated with the model and the underlying data, including potential bias, application context, and potential legal/regulatory impacts.

Data Assessment and Application:

Assess and document the quality and sources of the data to ensure that potential bias is addressed, and the data is suitable to support the model objectives.
  • Data used for training must be representative of the users who will interact with the system, safeguarding against discrimination and upholding privacy and security measures.
  • Following regulatory environment.
  • Ensure that data sources are reliable, and that data collection methods are ethical and respect privacy, including any required consent.
  • Assess data for potential biases and take steps to mitigate them.

System Design:

System designs will leverage best practices in ML Ops to ensure that the system is safe and reliable.
  • Document decisions and trade-offs made during system design.
  • Ensure that the system is robust.
  • Procedures are in place to monitor and audit its use.

Model Development:

Leverage best practices in model selection, training, and validation to mitigate bias and maximize transparency.
  • When possible, use Techniques (algorithms, training, and validation) that provide explainable outcomes with a focus on accuracy.
  • Qualitative and Quantitative.
  • Document decisions and trade-offs made during development and deployment.
  • Key components of the model and results of the development process will be documented.

Model Deployment:

Deploy models using best practices to maintain safety and security.
  • Models will be deployed in a proper MLOps framework.
  • Clear documentation of risk and limitations.
  • Regular monitoring to ensure it remains fair and unbiased as data and social norms evolve.

Communicating About Models and AI

Blend360 is committed to providing clear documentation and transparency for all AI Models and Systems to ensure well-informed application. We leverage Model Cards as a template to maintain and communicate information about all AI Systems that we create.

Model Details:

Basic information regarding the model version, type and other details.
  • Person or organization developing model.
  • Model date/version.
  • Model type.
  • Information about training algorithms, parameters, fair-ness constraints or other applied approaches, and features.
  • Paper or other resource for more information.
  • Citation details / License.
  • Where to send questions or comments about the model.

Intended Use:

Allows users to quickly grasp what the model should and should not be used for, and why it was created.
  • Primary intended uses.
  • Primary intended uses.


Summary of model performance across groups, instrumentation, and environments.
  • Relevant factors: What are foreseeable salient factors for which model performance may vary, and how were these determined?
  • Evaluation factors: Which factors are being reported, and why were these chosen? If the relevant factors and evaluation factors are different, why?


Chosen to reflect potential real-world impacts of the model.
  • Model performance measures.
  • Decision thresholds.
  • Variation approaches.

Training & Evaluation Data:

Considerations when training AI.
  • When possible, Training should mirror Evaluation Data.
  • If such detail is not possible, minimal allowable information should be provided here, such as details of the distribution over various factors in the training datasets.
  • Details on the dataset(s) used for the quantitative analyses in the card.
  • Datasets.
  • Motivation.
  • Preprocessing.

Ethical Considerations, Caveats, and Recommendations

How should the model be used (or not used)
  • Ethical concerns that could arise if the model is not used properly.
  • Indications of potential issues with fairness or privacy.
  • Proper use of underlying data to maintain security and efficacy of the model.

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