Artificial intelligence (AI) is transforming the world of business, offering unprecedented opportunities for innovation, efficiency, and competitiveness. However, AI also poses significant challenges and risks, such as ethical dilemmas, social impacts, and legal implications. Therefore, it is essential for businesses to adopt a responsible and ethical approach to AI development and deployment, balancing profitability and social responsibility.

In this blog post, we have outlined some of the key ethical considerations and best practices for implementing ethical AI in business. We have also provided some practical tips and examples on how to apply ethical AI principles in your own business context.

What are ethical considerations in AI?

Ethical considerations in AI refer to the moral and societal implications of creating and using AI systems. They involve evaluating the potential benefits and harms of AI for various stakeholders, such as customers, employees, partners, competitors, regulators, and society at large.

Some of the key ethical considerations in AI include:

  • Bias: AI systems can perpetuate and even amplify biases present in the data used to train them, resulting in unfair or discriminatory outcomes for certain groups or individuals. For example, an AI system that evaluates job applicants based on their resumes may favour candidates from certain backgrounds or genders over others.

  • Privacy: AI can collect and analyse vast amounts of personal data, raising concerns about privacy and data protection. For example, an AI system that tracks customer behaviour online may expose sensitive information or preferences that customers may not want to share or use for targeted advertising or marketing.

  • Transparency: AI systems can be complex and opaque, making it difficult to understand how they work or why they make certain decisions. For example, an AI system that recommends products or services to customers may not disclose the criteria or logic behind its recommendations or how it uses customer data.

  • Accountability: AI systems can have significant impacts on people’s lives and livelihoods, making it important to assign responsibility and liability for their actions and outcomes. For example, an AI system that drives a car may cause an accident or injury due to a malfunction or error.

  • Human-Centricity: AI systems should be designed to augment human capabilities and enhance societal well-being, rather than replace or harm humans. For example, an AI system that assists a doctor in diagnosing a patient should respect the doctor’s expertise and autonomy and support the patient’s dignity and consent.

Now it’s time to take a more microscopic approach towards ethical AI aspects. Let’s move on.

Fairness and Bias in AI

Approximately 40 percent of employees have encountered ethical issues related to AI use. Research Institute Capgemini defines ethical issues related to AI as interactions that result in unaccountable, unfair, or biased outcomes. 

Fairness in AI is about ensuring that the AI system provides equal opportunities to all individuals, regardless of their background or characteristics. Bias, on the other hand, refers to the tendency of an AI system to favour certain groups over others. Bias can creep into AI systems through various means, including biassed training data, biassed algorithms, or biassed interpretation of results.

Consider a hiring algorithm that is trained on a dataset where most successful candidates are male. The algorithm might learn to associate success with being male and unfairly disadvantage female candidates. To mitigate such biases, we can use techniques like bias correction and fairness-aware machine learning.

Bias correction involves modifying the training data or the learning algorithm to reduce bias. For instance, we can oversample underrepresented groups in the training data or apply regularisation techniques to prevent the learning algorithm from relying too heavily on certain features.

Fairness-aware machine learning, on the other hand, incorporates fairness constraints into the learning process. For example, we can modify the loss function of the learning algorithm to penalise unfair predictions.

Here’s a Python code snippet demonstrating how to use the fairlearn library to assess and mitigate bias in a machine learning model:

This code trains a logistic regression model with a fairness constraint that ensures demographic parity. The ExponentiatedGradient class implements a reduction approach to fair classification where a classifier is learned that optimises accuracy subject to fairness constraints.

Privacy and Security in AI

Privacy in AI refers to protecting individuals’ personal information from unauthorised access or disclosure. Security in AI involves protecting AI systems from attacks that could compromise their integrity or availability.

One of the biggest privacy concerns in AI is data privacy. With businesses collecting vast amounts of data to train their AI models, it’s crucial to implement measures that protect this data from unauthorised access and ensure that individuals’ privacy is respected.

Differential privacy is one such measure. It adds noise to the output of a function to protect an individual’s information. Here’s a Python code snippet using the diffprivlib library to train a differentially private logistic regression model:

This code trains a logistic regression model while ensuring differential privacy. The epsilon parameter controls the amount of noise added – smaller values provide more privacy but may reduce the accuracy of the model.

Security in AI involves protecting AI systems from attacks that could compromise their integrity or availability. One type of attack that has gained attention recently is adversarial attacks, where small perturbations are added to the input data to mislead the AI system.

Adversarial training is a technique used to make AI models more robust against such attacks. It involves training the model on adversarial examples along with the original data. Here’s a Python code snippet using the cleverhans library for adversarial training:

This code generates adversarial examples using the Fast Gradient Sign Method (FGSM) and then uses these examples for training. The epsilon parameter controls the magnitude of perturbations added – larger values produce more noticeable perturbations but may make the attack more successful.

In conclusion, as businesses continue to leverage AI for various applications, it’s crucial that they do so responsibly by considering these ethical aspects – fairness and bias, privacy and security – in their implementations. By doing so, they can not only ensure compliance with regulations but also build trust with their users and contribute positively to society.

How to implement ethical AI in business?

Implementing ethical AI in business requires a holistic approach that integrates ethics into every stage of the AI development and deployment process, from planning and design to testing and monitoring. It also requires a collaborative effort that involves various stakeholders, such as developers, users, managers, customers, partners, regulators, and society at large.

Here are some of the best practices and tips for implementing ethical AI in business:

1) Foster a culture

The first step to implementing ethical AI in business is to foster a culture and mindset of ethical AI among all the stakeholders involved in the AI development and deployment process. This means:

  • Raising awareness and education on the ethical implications and challenges of AI, as well as the ethical principles and guidelines that apply to AI use.

  • Encouraging dialogue and debate on the ethical dilemmas and trade-offs that may arise when using AI, as well as the potential solutions and alternatives that may be available.

  • Promoting ethical decision-making and behavior when using AI, such as following ethical codes of conduct, adhering to ethical standards and best practices, and reporting or addressing any ethical issues or concerns that may emerge.

  • Rewarding and recognizing ethical AI performance and outcomes, such as acknowledging and celebrating ethical AI achievements, providing feedback and incentives for ethical AI improvement, and holding accountable and correcting unethical AI actions or results.

2) Define

The second step to implementing ethical AI in business is to define and align your ethical AI vision and goals with your business strategy and values. This means:

  • Establishing a clear and compelling vision of what ethical AI means for your business, such as how it supports your mission, vision, values, and purpose, as well as how it benefits your customers, employees, partners, competitors, regulators, and society at large.

  • Setting specific and measurable goals for your ethical AI initiatives, such as what you want to achieve, how you want to achieve it, when you want to achieve it, and how you will measure your progress and success.

  • Aligning your ethical AI vision and goals with your business strategy and values, such as ensuring that they are consistent with your core competencies, competitive advantages, market opportunities, customer needs and expectations, stakeholder interests, and social responsibilities.

3) Assess

The third step to implementing ethical AI in business is to assess and mitigate the ethical risks and impacts of your AI solutions throughout their entire lifecycle. This means:

  • Conducting an ethical risk assessment of your AI solutions before, during, and after their development and deployment, such as identifying the potential sources, types, and levels of ethical risks, as well as the potential beneficiaries, victims, and affected parties of your AI solutions.

  • Implementing an ethical risk mitigation plan for your AI solutions before, during, and after their development and deployment, such as applying appropriate methods, tools, and techniques to prevent, reduce, or manage the ethical risks, as well as providing adequate safeguards, remedies, or compensations for the ethical harms or losses that may occur.

  • Monitoring and evaluating the ethical performance and outcomes of your AI solutions before, during, and after their development and deployment, such as collecting and analysing data and feedback on the actual or perceived ethical impacts of your AI solutions, as well as reviewing and improving your ethical risk assessment and mitigation plan accordingly.

4) Design

Design and develop your AI solutions with ethics in mind from the start. This means:

  • Applying a human-centric approach to your AI solutions, such as ensuring that they are aligned with human values, rights, and norms, as well as enhancing human capabilities and well-being, rather than replacing or harming humans.

  • Applying a user-centric approach to your AI solutions, such as ensuring that they are relevant, effective, and sustainable, meeting user needs and expectations, solving user problems, and creating user value.

  • Applying a data-centric approach to your AI solutions, such as ensuring that the data used to train, test, and run your AI solutions are accurate, complete, representative, diverse, and unbiased, as well as respecting the data privacy and security of the data owners and subjects.

  • Applying a quality-centric approach to your AI solutions, such as ensuring that they are reliable, robust, safe, secure, and scalable, as well as testing and validating their functionality, performance, and accuracy.

5) Communicate

Explain your AI solutions with transparency and clarity to all the stakeholders involved or affected by them. This means:

  • Disclosing the nature, purpose, and scope of your AI solutions, such as what they are, what they do, how they do it, why they do it, where they do it, when they do it, and who they do it for or with.

  • Disclosing the data sources, methods, and techniques used to create, train, test, and run your AI solutions, such as what data are used, how they are collected, processed, and analysed, what algorithms are used, how they are selected, designed, and optimized, and what metrics are used to measure their performance and accuracy.

  • Disclosing the criteria, logic, and rationale behind the decisions and actions of your AI solutions, such as how they make decisions or recommendations, why they make certain decisions or recommendations over others, what factors or variables influence their decisions or recommendations, and what assumptions or limitations underlie their decisions or recommendations.

  • Disclosing the risks, uncertainties, and limitations of your AI solutions, such as what potential errors or failures may occur, how likely or frequent they are, what are the possible consequences or impacts of them, and how they can be prevented or resolved.

6) Engage

Engage and collaborate with diverse and inclusive stakeholders throughout the AI development and deployment process. This means:

  • Identifying and involving the relevant stakeholders for your AI solutions, such as customers, employees, partners, competitors, regulators, and society at large, as well as ensuring that they represent a variety of perspectives, backgrounds, experiences, and interests.

  • Soliciting and incorporating feedback and input from the stakeholders for your AI solutions, such as asking for their opinions, preferences, expectations, concerns, or suggestions, as well as listening to their needs, problems, or values.

  • Empowering and enabling the stakeholders for your AI solutions, such as providing them with the necessary information, education, training, tools, or resources to understand, use, benefit from, or control your AI solutions, as well as respecting their autonomy, agency, and consent.

  • Co-creating and co-delivering value with the stakeholders for your AI solutions, such as working together to design, develop, test, deploy, monitor, evaluate, or improve your AI solutions, as well as sharing the benefits, costs, or risks of your AI solutions.


Ethical AI is not only a moral obligation but also a strategic imperative for businesses. By adopting a responsible and ethical approach to AI development and deployment, businesses can build trust and loyalty with customers, enhance reputation and brand image, reduce risks and costs, and innovate and grow.

We hope that this blog post has helped you gain a better understanding of how to leverage AI for good, while avoiding potential pitfalls and harms.

Written by: verbat

After its release, it didn’t take long for PHP to be considered as one of the most reliable open source technologies in the world. As a matter of fact, PHP stands at the forefront among companies offering open source development services. With great overall reliability and a huge community backing it, PHP is now a widely used server-side scripting language.

Complementing its many benefits is the presence of a broad range of tools – both free and premium that enable developers to get creative with the language.

In this blog, we present to you a list of a few great PHP tools that web developers would never regret using.


PhpStorm is a widely popular commercial IDE for PHP that can help streamline application development considerably. It offers integration with various popular PHP tools as well as relational databases, and supports widely used PHP frameworks and CMS solutions including but not limited to WordPress, Drupal, and Magento.

NetBeans Bundle for PHP

The PHP developer community welcomed the NetBeans Bundle for PHP with open arms. The bundle includes a plethora of great features – from semantic analysis with parameter highlighting to Symfony, Zend, and Yii framework support. In addition, it also supports code debugging with xdebug and unit testing with Selenium.


DebugBar is a PHP testing tool capable of identifying both HTML and JavaScript bugs. In addition, the tool is also capable of monitoring network traffic, evaluating JavaScript code, and inspecting CSS elements.


phpDox is a great solution for developers who require quick API documentation for a PHP application. The tool comes with a search feature and also offers information on code complexity and code coverage. In addition, developers can augment its functionality by adding more plugins.


RIPS originated as an open source tool but now is one of the leading security analysis solution for PHP. It’s a premium tool that offers consistently better threat analysis without false positives. RIPS code analysis is preferred by many developers to detect unknown security issues.

New Relic

New Relic is a great alternative to the already popular Retrace. It comes with thorough applied intelligence-powered performance monitoring capabilities in addition to infrastructure monitoring and user data analysis. Developers can use it to understand app performance dependencies and bottlenecks.

Aptana Studio

Aptana Studio claims to be the world’s most powerful open source web development IDE, and it has loads of features to back that claim. What’s so great about Aptana Studio is that it runs on Windows, Mac, and Linux. It has a built-in PHP server and debugging tool to build and test apps in one environment.

Sublime Text

Unlike most other tools in this list, Sublime Text is just a text editor. But a good one at that. The go-to-anything feature is what makes Sublime Text a winner for our developers. The text editor lets developers quickly locate lines of code, and allows simultaneous editing – change multiple code instances at once. It’s not a free tool though. But a one-time fee of $80 makes it a wallet-friendly tool for developers.


There aren’t many developers out there who haven’t heard about Selenium. It’s a lightweight, open source testing framework compatible with the most popular browsers. Selenium allows users to create their own custom UI tests in any language and can also automate certain web-based administration tasks. It’s also a favorite for many companies offering Agile software development services.


There seems to be an abundance of open source PHP solutions that developers can choose from. We know that this list above is not complete, and requires quite a few other mentions. We simply wanted to keep things short and mention the ones that we personally know are worthwhile. We are sure the tools mentioned in this blog can aid any software development company specializing in PHP development.

Image created by GraphiqaStock –

Written by: verbat

Even SMBs have started realizing the benefits of test automation while myths and misconceptions surrounding the core concept of test automation keeps increasing. Contrary to popular beliefs, the advent of test automation techniques didn’t affect the demand for manual software testing. Organizations simply realized that a combined Manual – Automated testing practice grant greater benefits provided it’s implemented thoughtfully and effectively.

Though test automation essentially reduces the time to deliver high-quality products, it doesn’t always lead to quality success. Many organizations that are interested in test automation or are planning to invest in the practice may not be fully aware of its limitations.  There are hidden costs in implementing a complete test automation strategy.

From hiring QA professionals/Quality Engineers to test management and automation environment maintenance, it may overwhelm businesses that aren’t prepared to completely invest in the technology without preparing for it beforehand. This blog is for those organizations that are planning to jump on the test automation bandwagon, and discusses the various limitations of test automation.

Test cases should be designed for repeatability

Many organizations tend to implement test automation whenever they feel like accelerating quality feedback. This is possible only if the test cases are created for repeated use. Test automation is not very easy to set up either, demanding the technical team to invest hours in setting up, troubleshooting, and maintaining it.

If the test cases can’t run repeatedly even after exhausting a lot of resources setting up automation, leveraging test automation won’t grant much benefits at all. The key is to prioritize repeatability for maximum ROI from your test automation strategy.

Relying too much on test automation can do more harm than good

You may have read about organizations relying on test automation to scale product quality processes. This is more common in companies offering Agile software development services, and it works. You can get streamlined, much more efficient development cycles.

However, relying on test automation too much can end up causing a lot of issues, primarily due to the fact that test automation simply doesn’t apply to all kinds of test cases especially when scaling QA processes in an Agile ecosystem. A more balanced approach combining both test automation and manual testing offers better chances of scaling success. There is also the fact that a product that’s constantly evolving with each sprint will need you to allocate more resources just to maintain automation test scripts.

Test automation demands serious expertise & technical skills

Just because you have automated test scripts and a team with basic knowledge of automated testing doesn’t mean you can implement test automation and reap the best of its benefits. It requires a high level of expertise and technical skills to write test scripts. As a matter of fact, the technical skill requirement is one of the biggest limitations of test automation. You won’t easily find an expert automated tester like you would find an expert in software development.


With all the limitations we have discussed right now, we still can’t conclude that test automation isn’t a worthy investment. It is a big move that demands big investments and total dedication for a medium-sized business with limited resources. Nevertheless, if the business has the right talent and a great strategy, they won’t ever regret investing in test automation.

Written by: Dev Hariharan

Incorrect planning of a project will result in an end product that’s not in line with customer expectations subsequently impacting the software development company’s reputation. Most project managers would face a similar situation at least once in their career. Add budget overflows and delays into the mix, and the whole project might end up as a disaster.

A bit of flexibility can open doors to solve such project management issues. Agile gives that flexibility you need.

Most agile software development services today make use of SCRUM – an iterative framework that considerably reduces the risks associated with project development. How it requires both the team and the customer to collaborate through the project’s lifecycle, so the plan works out while staying in budget.


It’s basically a set of procedures to iteratively and incrementally manage software development processes from various tasks. So essentially, each iteration or sprint, based on the customer’s feedback, will refine the product. The customer will be able to guide the project in the right direction, tweaking it along the way if necessary. This reduces the chances of the product failing to meet expectations in the end.

Additionally, the framework also offers a good amount of flexibility for a cyclical estimate of the project. Though not a complete and precise estimate, this will still keep the development company and the customer safe from misleading estimates. Activities for each sprint can be added or removed depending on the customer’s budget.

Risk Management with SCRUM

Project managers can adopt various SCRUM practices to minimize and effectively manage risks that may occur during development.

Here are a few such practices to give you a better idea.

  • Flexibility mitigates business-related risks: As mentioned earlier, SCRUM offers the flexibility to add or modify requirements whenever necessary in the product development lifecycle. This gives the company a window to deal with unforeseen threats or unexpected opportunities from the business ecosystem whenever they show up, and at a comparatively low cost. Traditional project management methodologies won’t be that effective in such scenarios.
  • Regular feedback from customers mitigates expectations-related risks: The whole project can be guided by the customer. Expectations can be set after each sprint based on customer feedback. This reduces the likelihood of risks due to miscommunication, and ensures the stakeholders that the project has a better chance of meeting expectations in the end.
  • SCRUM team reduces estimation risks: The SCRUM team will take responsibility for the backlogs in each sprint, and will manage accordingly to ensure timely delivery of the product. They will also be providing cyclic estimates. This considerably reduces estimation risks.
  • Transparency facilitates early detection of risks: SCRUM framework is designed to be transparent. This transparency helps the team identify the risks early in the initial stage of development itself, and rectify them. Challenges and obstacles for each team will be discussed and logged during Scrum of Scrum meetings after each sprint. This also enables the team to handle the risks without raising the alarm.
  • Continuous delivery reduces investment risks: SCRUM is an iterative framework. Unlike traditional project management methodologies, the project won’t be delivered at the end of the development lifecycle. The customer will be able to see how the project is going, and the changes made during each iteration. This reduces investment risks.



Risks will always be present regardless of the project management methodology chosen. SCRUM only affects the likelihood and the impact of those risks. To conclude, SCRUM is more like a painkiller instead of an antidote. If pain is the issue and risks are what’s causing it, SCRUM relieves you of that pain but doesn’t neutralize the risks – risk management and not risk elimination.

Written by: Prashant Thomas

Faster time-to-market and impressive software quality are more important than ever. This is why more businesses have started to invest in DevOps, especially when it comes to software testing. To ensure quality and timely delivery of products or services, collaboration is key. A DevOps environment has that collaborative approach when handling new-age applications, giving them real-time solutions. Continuous development and testing is another benefit.

This modular approach, if not a cultural shift, has now become essential for a business that provides Agile software development services enabling them to provide sustainable, robust, and innovative digital solutions. As DevOps-driven testing has started moving to unexplored domains, it’s important to keep an eye out for new trends. These trends will define better approaches and more efficient processes for leveraging DevOps.

Here are a few DevOps testing trends that give the most hope this year.

Failure is acceptable if it’s early

In a DevOps environment, testers can start testing the product from the early stages of development itself. This way, defects and other errors can be identified and rectified early subsequently reducing the risks when the application finally enters the market.

Simultaneous development and testing

Both developers and testers are responsible for ensuring quality. In a DevOps environment, development and testing should go hand in hand. This concurrency is one factor that reduces the time-to-market, and also one of the reasons why the IaC (Infrastructure as Code) concept thrives in a DevOps ecosystem.

Continuous testing and delivery

Continuous and quick deployment is probably the biggest benefit of adopting DevOps practices. This makes continuous testing and delivery important as well. Continuous development, testing, and delivery enable enterprises to easily adapt to digital transformation and come up with innovative ways to add quality.

Shifting to the Cloud

Cloud technology entirely transformed the software development and testing processes. The cloud made it possible for testers to access application data from anywhere at any time. At present, cloud also plays a role in implementing DevOps practices effectively in an organization, and is expected to boost continuous development and testing in a DevOps ecosystem even further in the near future.

Written by: verbat