AI has been making waves in the world for years now, and as a result, there’s an increasing need to regulate how it can be used. In 2021 across Europe, the Asia-Pacific region among other places regulating bodies is working hard on coming up with rules that will help keep track of what goes into your algorithm while also giving people some peace knowing their data isn’t being misused or shared without permission (or worse). The U..S however seems less fortunate when compared to other countries who have taken this step but nonetheless we should still strive forward because one day these regulations might come our way too!
This is understandable given the sensitivity of many people’s data, but it also highlights how important it is for companies to have a plan in place for managing their data assets. Here are five tips to help you get started:
- Understand what data you have and where it came from: This seems like an obvious one, but you’d be surprised at how many companies don’t really know what kind of data they have or where it came from. Make sure you understand what each dataset contains and how it was collected so you can make sure it’s being used correctly.
- Develop a governance framework: This will help ensure that everyone in your organization knows who is responsible for managing different types of data and what the expectations are for how that data should be used.
- Create a centralized repository: This will make it easier for people to find the data they need and will help you keep track of what’s been updated and where.
- Know your tools: There are a variety of different tools out there that can help you manage your data assets, so it’s important to know which ones will work best for your needs.
- Be prepared to change: As your organization grows and changes, so too will your data management needs. Be prepared to adapt your approach as needed.
The debate on regulating AI has been a hot topic this year, but with many different angles from which to approach it. One of the most prominent discussions regards Europe and UK-based companies who want access restrictions lifted so they can better employ their own innovations in machine learning algorithms while also remaining compliant at home; however, there’s major pushback against such proposals by privacy advocates because we don’t know what kind or how much data will be collected under certain circumstances when you’re not aware something even exists - let alone consenting agreement for its use beforehand!
Europe and the UK: Paving the way for AI regulation
Europe has been a hotbed for regulation as of late, and it’s no exception in the field that deals with AI. The European Commission announced an initiative earlier this year to help enterprises monitor their systems more effectively using machine learning (ML). They also created standards about what kind of information should be available when you’re reviewing algorithms - something has been known scientifically referred to as “algorithmic transparency.” In another move from London-based businesses who want better guidelines surrounding how these technologies are used; Britain currently enforces several laws related specifically to auditing practices among others things like assurance & accountability which will hopefully lead to broader adoption across all sectors.
The goal is to make it so that businesses have a better understanding of how they can use machine learning technologies, as well as what benefits and risks are associated with doing so. This will help organizations make more informed decisions about whether or not to implement these solutions in the first place. There are already many different types of software that offer some form of monitoring or analysis using machine learning. However, these products are often difficult for businesses to understand and use effectively. The new standards aim to change that by providing clear guidance on what data should be collected and how it should be used.
It is hoped that by making these standards available, more businesses will be able to take advantage of machine learning technologies and reap the benefits that they can provide. In turn, this could lead to more investment in these technologies and further innovation in the field.
The new standards are a welcome addition to the machine learning landscape. However, it is important to remember that they are only guidelines. businesses should still exercise caution when implementing any new technology, regardless of how well it conforms to these standards. After all, the goal is to make informed decisions that will improve business outcomes - not blindly follow rules that may or may not be beneficial in the long run. But with these standards in place, businesses now have a good starting point for making those decisions.
Movement at the state and local levels in the U.S.
n the United States, states and local governments have begun to pass accountable AI regulations. In Colorado specifically, the state legislature created SB21-169 Restrict Insurers’ Use of External Consumer Data which ensures insurance companies are held accountable for their discriminatory practices when using external data sources such as credit scoring models or facial recognition software systems among other things they could use it on but don’t because we’re not allowed access! If an insurance company is shown to have used such a system in a manner that resulted in higher premiums or denied coverage, they could be fined up to $100,000 per violation.
This is good news for those of us who have been concerned about the potential misuse of AI by insurance companies. It’s a step in the right direction towards regulating AI and ensuring that it is used responsibly. However, there is still more work to be done in this area. For example, this bill does not address the use of AI by other industries such as healthcare or finance. Additionally, it only applies to Colorado so other states will need to pass similar legislation if we want to see nationwide change.
U.S. federal agencies take aim at decentralized AI governance
The National Security Commission and Government Accountability Office (GAO) have submitted a final report to Congress recommending that the government take action in promoting more trustworthy AI. Highlighting public trust issues with national security, intelligence community law enforcement entities are encouraged by private sector actors like Google who led this summer’s project on how we can guarantee accountability for responsible use of artificial technologies
The two organizations’ most recent output calls out both corporate America as well as our own country’s regulatory bodies when formulating policies surrounding these newish technologies—a trend which seems all too common nowadays given what takes place around the world of AI on a daily basis. No matter how many blog posts, articles, or news stories come out about the potential risks and dangers of artificial intelligence, it seems that not enough people are reading or taking heed to them. It’s as if we’re all too comfortable trusting in these machines, even when we know that we shouldn’t.
This is where government intervention is absolutely necessary in order to help ensure that responsible use of AI is adhered to by both public and private entities. In recent months, there have been several high-profile cases of companies using AI irresponsibly—and sometimes dangerously. The National Security Commission and Government Accountability Office (GAO) have submitted a final report to Congress recommending that the government take action in promoting more trustworthy AI. Highlighting public trust issues with national security, intelligence community law enforcement entities are encouraged by private sector actors like Google who led this summer’s project on how we can guarantee accountability for responsible use of artificial technologies
The two organizations’ most recent output calls out both corporate America as well as our own country’s regulatory bodies when formulating policies surrounding these newish technologies—a trend which seems all too common nowadays given what takes place around the world. In order to combat this, the government should do what it can to ensure that private entities are adhering to best practices in AI development and certification. Furthermore, the government should also be clear about its own policies on data use, biometrics, and other information that may be used to train these systems.
The end goal is for the general public to have confidence that the government is taking steps to make sure AI technologies are being developed responsibly and with their best interests in mind. Only then can we hope to see wider adoption of these tools and technologies across all sectors.
Private sector actors like Google have taken initiative in promoting trustworthy AI; however, more needs to be done by both corporate America and our own country’s government if we want to maintain global leadership in this field. What’s more, these efforts must extend beyond the United States if we wish to see responsible AI development on a global scale. Ultimately, it is up to all of us — government, industry, and civil society — to work together to ensure that artificial intelligence technologies are developed responsibly and for the benefit of all.
What to expect this year
In 2021, we saw a major shift toward regulating AI across the globe. The importance of safeguarding these systems is becoming more prevalent as they are used by corporations and governments to make important decisions that can impact entire economies or populations- in other words: there’s money at stake! Expect this trend to continue into 2022 with discussions centered around how best to protect our future generations against possible risks associated with artificial intelligence (AI). One proposal could be implementing ethical standards for hiring practices so developers don’t go wrong when designing their products while another idea might call out specific countries like China that have been suspected of using algorithms intended to control and manipulate their citizens.