FICO and Corinium release State of Responsible AI report
FICO, a global analytics software firm, released its State of Responsible AI report from market intelligence firm Corinium which found that despite the increased demand and use of AI tools, almost two-thirds (65%) of respondents' companies can't explain how specific AI model decisions or predictions are made.
This global survey of 100 C-level data and analytics leaders revealed the different issues AI-focused executives are considering and tackling to prepare their organisations to be AI-enabled in an ethical way.
The study found that the lack of awareness of how AI is being used and whether it's being used responsibly is concerning as 39% of board members and 33% of executive teams have an incomplete understanding of AI ethics.
How can businesses combat AI bias?
The survey found that currently, only a fifth of respondents (20%) actively monitor their models in production for fairness and ethics, while less than a quarter (22%) say their organisation has an AI ethics board to consider questions on AI ethics and fairness. One in three (33%) have a model validation team to assess newly developed models and only 38% say they have data bias mitigation steps built into model development processes.
However, evaluating the fairness of model outcomes is the most popular safeguard in the business community today, with 59% of respondents saying they do this to detect model bias. Additionally, 55% say they isolate and assess latent model features for bias, and half (50%) say they have a codified mathematical definition for data bias and actively check for bias in unstructured data sources.
The majority of businesses (90%) agree that inefficient processes for model monitoring represent a barrier to AI adoption. 63% of respondents believe that AI ethics and responsible AI will become a core element of their organisation's strategy within two years.
"Over the past 15 months, more and more businesses have been investing in AI tools, but have not elevated the importance of AI governance and responsible AI to the boardroom level," said Scott Zoldi, Chief Analytics Officer at FICO. "Organisations are increasingly leveraging AI to automate key processes that - in some cases - are making life-altering decisions for their customers and stakeholders. Senior leadership and boards must understand and enforce auditable, immutable AI model governance and product model monitoring to ensure that the decisions are accountable, fair, transparent, and responsible."
The report highlights practices that will help organisations plan a route towards responsible AI, including:
- Establishing practices that protect the business against reputational threats from irresponsible AI use
- Balancing the need to be responsible with the need to bring new innovations to market quickly
- Securing executive support for prioritising AI ethics and responsible AI practices
- Futureproofing company policies in anticipation of stricter regulations around AI
- Securing the necessary resources to ensure AI systems are developed and managed responsibly
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.