Responsible AI

As business leaders, we know that #DeepLearningAI has enormous power to expand access to opportunity. With great power; however, comes great responsibility. That’s why, as a community, we need to always keep in mind ethics and our shared values as we develop exciting new applications.

Collective[i], the company behind Intelligence.com, The Modern Sale, Collective[i] ForecastTM, and a leading AI-enabled platform for revenue leaders, prioritizes ethical AI.

We believe that the responsible and ethical use of #AI is designed to elevate us and allow us to operate more  effectively. Great AI creates more rewarding work. It’s not meant to replace people. For example, Collective[i]’s enterprise application removes menial work (like CRM data entry and sales forecasting) to free up time for sales professionals to build meaningful relationships that lead to revenue.

In the spirit of transparency, here are five areas to consider in the development of our technology and worthy of your consideration as a user.

1. Bias in training data: One major pitfall of #DeepLearningAI is the potential for bias to be encoded into the model stemming from biases present in the training data. If a model is trained on a dataset that is predominantly male, for example, it may perform poorly on tasks related to female individuals. That’s why it’s essential to ensure that your training data is diverse and representative of the population. To remove as much bias as possible, Collective[i] captures data from our diverse community and leverages  diverse talent to train and build our AI. We recognize that bias is the norm and we aim to mitigate it by  inclusion of a wide range of data sources and perspectives.
2. Lack of interpretability: Another ethical concern with #DeepLearningAI is that it’s a “black box”, meaning it’s almost impossible to reverse engineer the thinking (and the specific data) that led to an output. Because deep learning models are complex and operate by learning patterns in data, it can be tough to understand how they arrived at a particular decision or prediction. This lack of interpretability can make it challenging to identify and correct any biases or errors in the model. Collective[i]’s approach to this challenge is to test our model against known outcomes. For example, we will do retroactive spot checks for forecasting to see if the predictions matched the reality. We also rely on broad datasets so that the AI learns from as many perspectives as possible. Lastly, we host a discord channel so that people can share and monitor anomalies for our team to research. .
3. Privacy: #DeepLearningAI systems are often trained on large datasets which raises concerns about the privacy of individuals whose data is analyzed. It’s essential to ensure that the data used to train AI models is collected and processed ethically, and that proper measures are taken to protect the privacy of individuals. Collective[i] is committed to protecting the privacy of individuals by following best practices for data handling and security. For more information on our privacy program and philosophy of data minimization, please visit our Trust Center.
4. Transparency: To address some of the ethical concerns outlined above, it is important for AI developers to be transparent about the development and use of their systems. This includes providing information about the training data, the performance of the model, and any biases that may be present. We also encourage our community to share via Discord and other channels areas where they would like to better understand how we developed particular features and/or functionality.
5. Responsible use: As with any technology, the ethical use of #DeepLearningAI depends on the responsible application of the technology by its users. This includes considering the potential impacts of the technology on society and taking steps to mitigate any negative consequences. Collective[i] is committed to the responsible use of AI. As we build new features, our goal is to design them to prevent nefarious use. That said, we are one company and cannot imagine or prevent all of the possibilities for abuse so we encourage our community to share feedback and report bad actors.

The ethical use of #DeepLearningAI requires careful consideration of a range of issues, among other things, bias in training data, lack of interpretability, privacy, transparency, and responsible use. Through awareness and our actions, we can ensure that #AI is developed and used in a way that benefits our community and society as a whole. #EthicalAI #ResponsibleAI