Health Impact Attribution. Using Machine Learning and Artificial Intelligence for Disease Attribution, Financial Impact Modeling and Compensation in Real Time.

The health impact of organizations, food companies, transportation companies and other companies is ripe for further study and the potential use of AI and Machine Learning for real time attribution modeling to determine which companies are putting toxic substances such as sugar, unhealthy foods, pollutants and other disease inducing substances into the human ecosystem. This is not a replacement for personal responsibility just a way to compensate humans in real time for limited food and other choices based on income, etc.

Easy political targets such as Tobacco companies are usually the first to gain the ire of the political and legal systems. However, the legal system represents and inefficient method of reintroducing value back into the human consumption ecosystem and rather is often a way for government entities and legal professionals to enrich themselves at the expense of public health.

AI and Machine Learning with the right data offer the potential to measure impacts of producers on consumers in real time and extract a portion of any sale price for reintroduction into the human consumption pool to pay for say personal health care and other costs incurred by consumers of these products. While taxes are often used for this purpose the removal of a distribution intermediary such as governments creates a faster return of wealth to the human consuming or impacted by toxic offerings.

Some areas of focus for attribution modeling and disease reduction are:

1. Fast Food. The use of Machine Learning, Big Data and Artificial Intelligence has the capacity to create real time measures of the impact of low quality, poor nutrition food substances on the human health collective. Companies that choose to distribute these products can then be assigned an impact figure that totals their daily, monthly and yearly impact to the long term health of the human population. This opens up the potential for determining ways for real time wealth return to the humans who ingest these products.
2. Automobiles. Automobiles driven by humans are one of the most dangerous activities in the world killing over 40,000 people annually in the United States. While automated vehicles will reduce this figure dramatically until that is a reality we are stuck in a world with wildly unpredictable driving habits and safety conditions.
3. Heart Disease. By regressing a persons consumption content it is possible to use attribution modeling for the impact of goods consumed by that person over their lifetime. Data derived from shopping habits, disease, healthcare and mortality data and other data patterns can be used to determine just what impact and who introduced the harmful products to that particular human and offer real time compensation into that humans consumption account.
4. Cancer. Similarly Cancer can be often regressed to consumption patterns for certain types of cancer and attribution modeling can be used to extract real time wealth from the producers of these products.
5. Pollution. This is a well documented impact on human health and as data continues to proliferate the real time impact resolution can result in automatic credits to human accounts from polluters as compensation for the impact of polluters in their region.
6. Media. There is strong evidence that the media, particular prior to the Internet when media was more concentrated, has an impact on human health. Whether it is excessive violence, stress from repeated messaging about real or imagined conflict scenarios, politically divisive messaging played on repeat, or other potentially toxic media products, these all potentially have an impact on human health and offer the opportunity to model mental health impacts and other health related impacts based on viewing habits, location, etc.

These are just some of the scenarios for using Machine Learning and AI to create real time adjustments to the human wealth exchange and correct inefficiencies or systemic imbalances created by one sided power structures, inefficient compensation systems and other factors. While I am not arguing for removal of personal choice, I am arguing for using data and ML for removing intermediaries and correcting human impact activities, particularly in the future when currency, jobs and capitalism are not central to the human experience.

Note: All positions in this article are the position and research of the author and not attributed to any author affiliated companies, groups or organizations.