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How to Ensure Ethical Artificial Intelligence with Your SAP Security Strategy
With any new technology, it is important to consider the potential risks and threats it can bring, and to develop a solid strategy for avoiding them before that technology is widely used.
While some visionary minds, such as Elon Musk and Stephen Hawking, have issued warnings about the longer-term existential risks AI can pose to society, in the near term, organizations must consider the general impact AI can have on business operations.
For example, automated decision making that doesn’t consider extenuating circumstances, the perception of misuse of personal data, and machine learning based on faulty data can significantly affect the security of a company’s business model and reputation.
This article shows security and risk managers and decision makers who are considering the use of AI in their organization’s software system landscape how to mitigate the risks posed by AI software by expanding existing security standards with a clearly defined set of digital ethics.
Mitigating measures for these scenarios would be to include the event (the hurricane or water shortage, for instance) in the price-finding algorithm (via automated rules or a rule that triggers an alert for human intervention, for example) and provide unambiguous instructions for handling the situation.
This type of scenario must be addressed by additional standards, and may include some academic research to understand how factors such as cultural backgrounds can impact ethical requirements, and how technological solutions can mitigate the impact.
While this might not be a huge problem in the case of simple image recognition, what happens if it’s a neural network that a business uses to run prediction routines, and that managers rely on to make decisions?
If there is fraudulent intent involved, it can be hard to identify or prove, since technologies such as neural networks do not work as transparently as previously used analytical algorithms —
One way SAP is taking action to address these challenges is by participating in broad discussions about establishing norms around the use of AI-based software that range across industries, academics, and politics.
Another way SAP is addressing these challenges is by creating a framework of ethical values that goes beyond legal compliance to take into account the effect AI-based software can have on people’s quality of life.
There are three tasks in particular that are key to paving the way to ethical, sustainable AI practices based on guiding principles similar to the ones outlined by SAP: gathering requirements for ensuring the implementation of ethical AI, adding ethical AI information to your existing standards and policies, and monitoring and auditing AI activities.
These types of requirements must be gathered in close collaboration with customers, AI providers, and people who handle business ethics questions (executive leaders, portfolio or compliance managers, and sustainability departments, for instance).
Checklist items should include questions related to human involvement in AI, such as the end user’s cultural values, how the end user’s current context is evaluated, and situations in which the end user will want AI functionality turned off.
Checklist items should also consider legal compliance requirements (such as data privacy regulations), how to unveil hidden override directives, and how to assess the potential long-term impact of AI operations.
The information must include patterns for human-machine interaction in specific situations, customization parameters to fulfill specific cultural requirements, and procedures to overrule AI (if reasonable and secure).
For example, automated controls could monitor price-finding algorithms for scenarios such as water shortages and apply rules for handling cases in which human health might be affected (to stop any automated price increases, for instance).
In general, the process of AI auditing will be of high interest to insurance agents and lawyers when it comes to liabilities based on a system’s decisions and proposals, but it is also relevant and useful for any organization that is planning on using AI-based software.
It is good practice to be proactive about how to mitigate these risks using additional security-related measures, such as implementing random reviews of AI behaviors, controls, and audits, or specifying human-machine interaction schemas for situations in which someone must make a decision relevant to a person’s ethical attitude.
When it comes to AI-based software, to ensure the lowest possible level of risk for human life and the highest possible level of integrity in modern enterprises, it is crucial to have security management policies in place.
Just as effective security management requires collaboration across company lines, implementing a successful digital ethics strategy for AI requires integrating ethical principles across your organizational standards, policies, and behaviors.
Building Stock Selection into an Artificial Intelligence Framework
Day-trading profits are being sucked out of the market by intelligent systems that trade in incredibly small time-frames.
Such a framework could put powerful Machine Learning algorithms into the user’s hand and potentially even allow the user to hand-craft strategies and automate their trading decisions.
Maybe you’re less interested in diving too deep and follow the mainstay metrics like P/E ratio, P/B ratio, D/E ratio, div yield, and earnings growth.
An AI-driven system that makes decisions based on your personal parameters for valuing a stock should be able to build your portfolio at t=0 and increase or decrease positions based on adherence to your parameters outlined at model inception.
One can play with their network and add layers, add or remove neurons in the hidden layers, but ultimately the input and output layers are well determined.
We automate the system to fit these models to the 100 stocks each time the metrics are updated (so probably whenever the company reports earnings).
If we decide a 75% threshold for justifying a buy decision then maybe those stocks with above a 75% position for hold receive a certain percentage of our allocated capital.
In this case, you’ve seen your profits shrink from about $50,000/yr (at .2% over 200 trading days for a starting portfolio of $100,000) to about $22,000/yr (at .02% over 200 trading days for a starting portfolio of $100,000).
Many firms are running into this issue where their strategy to stay competitive is to squeeze every possible bit of profits out of their positions without increasing risk.
Maybe you are exceptionally competent and have set up a system of alerts for your 20 favorite stocks and trading moving averages (and potentially other indicators as well) during key moments like crossovers.
Much like an operator in a factory watching the human-machine interfaces controlling some process using automation, you are now the operator ensuring the machines operating your portfolio are not misbehaving.
But in a world where quant funds are outperforming all of the traditional investing firms, it’s hard to believe that the desire for profits will not drive more firms to mimic the behavior of quant funds.
think it is realistic to imagine a future where markets are almost completely dominated by AI, algorithmic trading, and machine-driven market making and order matching.
I don’t think it’s too fanatical to claim that within the next few decades the vast majority of market participants will be Artificial Intelligence that represent the intentions of their makers.
I think this means AI will exist that can digest massively large amounts of market signals and data that a normal human PM could never dream of analyzing.
When AI dominate the market, any trader (human or otherwise) that looks to make a trade will send a signal that all the algorithms operating in the world will receive and digest accordingly.
Crafting models and simulating their performance is addicting and I encourage anyone well-versed in machine learning, deep learning, etc to have a stab at turning traditional value investing and technical analysis into broader Machine Learning based strategies.
An Artificial Intelligence Platform for Network-wide Congestion Detection and Prediction Using Multi-source Data – C2SMART Home
Typical approaches for congestion detection include Global Positioning System (GPS) trace analysis, use of back propagation (BP) neural networks and Markov models, real-time adaptive background extraction, undedicated mobile phone data analysis, space-time scan statistics (STSS) based non-recurrent congestion (NRC) detection, etc.
Several congestion prediction methods have also been developed such as adaptive data-driven real-time congestion prediction, traffic flow prediction using floating car trajectory data, Bayesian network analysis, deep learning theory, data mining based approaches (integration of K-means clustering, decision trees, and neural networks), Hierarchical fuzzy rule-based systems optimized with genetic algorithms, etc.
These existing studies have made significant contributions to development of the methodologies and technologies for traffic congestion detection and prediction, but with the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT) technologies, new challenges and opportunities are continuously emerging with higher requirements for metrics such as detection and prediction accuracy, real-time results, and stability.
AI for Earth Innovation
To address the many pressing scientific questions and environmental challenges facing our planet, we must increase global understanding of how human activity is affecting natural systems and create a community of change, driven by data and cutting-edge technology.
The National Geographic Society and Microsoft’s AI for Earth program are partnering to support novel projects that create and deploy AI tools to improve the way we monitor, model, and ultimately manage Earth’s natural systems for a more sustainable future.
Extreme weather events, rising sea levels, higher global temperatures, and increased ocean acidity threaten human health, infrastructure, and the natural systems we rely on for life itself.
For example, pair an expert capable of creating and deploying an open source trained model or algorithm with an environmental research specialist using this model or algorithm to address a pressing environmental question.
- On 23. januar 2021
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