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Machine Learning and Whether It Is Doing More Harm Than Good

Machine learning is a vital technology.

Machine LearningEvery business and organization needs to consider it as part of their operations.

In fact, machine learning already exists in our daily lives.

You can find it underlying your Facebook feed and Amazon product recommendations.

These are just two common examples of machine learning.

They exist in complex data analysis that organizations carry out.

In our data-driven world, machine learning is a term we must explore.

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So, What Is Machine Learning?

Machine learning is a subset of artificial intelligence.

At the core of machine learning are algorithms.

This gives the capability to computers to self-learn.

In other words, computers do not have to be explicitly programmed to know something.

It is a method of data analysis that automates analytical model building.

Therefore, programs can automatically learn, grow, change and develop when they analyze new and past data.

When scanning through data, it executes pattern recognition.

Machine learning is not new.

It has been around since the 1930s.

Alan TuringIt all began with Alan Turing, a well-known artificial intelligence expert.

His exploration of ‘neural networks’ led to machine learning as we know it today.

Over the past years, the term machine learning has been gaining momentum.

More people are realizing the potential of machine learning.

Its algorithms enables accelerated predictions and decisions.

The end results are complex statistical analysis.

Here is the full description of machine learning:

  1. Why Are People Increasingly Interested in Machine Learning?
  2. Is Machine Learning a Good Choice?
  3. Machine Learning Pros
    – Intuitive Search Experience
    – Dynamic Pricing
    – Fraud Detection
  4. Machine Learning Cons
    – Humanity and Human Judgment Erodes
    – Exposed to Human Bias
    – Widen the Digital Divide
  5. The Future for Machine Learning

Why Are People Increasingly Interested in Machine Learning?

Why Are People Increasingly Interested in Machine Learning?

There are a few reasons that machine learning is increasing in popularity.

Firstly, there are more data today than there has ever been.

Hence, making sense of the data clutter is of importance to organizations.

Secondly, computational processing is cheaper.

Thirdly, data storage is more powerful and affordable.

All of these put together makes the interest in machine learning to increase.

The lower costs of implementing machine learning are boosting its development.

Organizations can now analyze bigger and more complex data.

As a result, individuals and organizations receive faster and accurate data.

In fact, these are high-value predictions.

The best thing about it is they require minimal human intervention.

In a data-driven time, machine learning plays a huge role.

What was once too expensive and too time-consuming is now possible.

This capability can help individuals and organizations to make better and smarter decisions in real time.

Is Machine Learning a Good Choice?

Is Machine Learning a Good Choice?

If implemented accurately, machine learning can benefit many industries.

It has the potential to add revenue or reduce dollar spending.

The potential it holds is huge because it can provide new insights through data analysis.

Every organization has a humongous volume of data.

This includes data about purchases, customer demographics, search data and inventory information.

One thing we must acknowledge is technologies cannot replace human intuition.

However, too many resources had been spent in the past to train the workforce.

Even with a dedicated task force,  quality, speed, and accuracy still suffer.

Therefore, machine learning provides a solution for these problems.

It can analyze a huge amount of data.

The best thing is it can run continuously with minimal human intervention.

This can be a major competitive advantage for companies.

Despite that, there have been some major criticisms around machine learning.

To understand things better, let’s look at both sides of the coin.

Firstly, let’s analyze the advantages of machine learning.

Machine Learning Pros

Pros

  1. Intuitive search experience
  2. Dynamic pricing
  3. Fraud detection

Earlier on, we’ve had a glimpse of the capabilities of machine learning.

Now, let’s take a in-depth look at its capabilities.

1. Intuitive Search Experience

As a customer, it is impossible for us to understand all the information available.

This is where machine learning does the heavy-lifting for us.

It can help us figure out what is most relevant to us.

Merging machine learning as part of content delivery has made this possible.

The end user can obtain the right content, in the right form and  at the right time.

In fact, Google has been implementing machine learning.

The organization uses it to write featured descriptions and use “sentence compression algorithms” on desktop results.

It gives brands the ability to go through a massive amount of data.

Ultimately, this helps create actionable strategies.

Additionally, it helps them to understand the customer journey.

Today, a customer decision process is no longer predictable.

Brands do not hold as much impact as they used to.

Instead, customers often turn to the Internet to find solutions.

Therefore, machine learning creates smart content.

It personalizes the search experience of consumers.

Hence, it helps to address the unique needs and intent of an individual.

From this, brands can detect changes in interest and consumption behavior.

Ultimately, this enables organizations to remain competitive and remain at the forefront of their industries.

2. Dynamic Pricing

When it comes to pricing your products, it can seem deceptively easy.

Typically, organizations will survey the price that their competitors offer.

From there, they decide whether they want to match or beat the price.

There is one limitation to this approach.

It is only applicable where price transparency and comparison is easy.

What happens when organizations don’t have readily available comparisons?

Even if organizations have all the data, they still struggle to price their products or services.

This is where machine learning aims to fill the gap.

It does this by introducing the concept of dynamic pricing.

This optimizes prices for the individual in real time.

All of this can be done easily.

There is no need to manually define or a test complex pricing tools.

Instead, dynamic pricing utilizes machine learning algorithms.

With machine learning, users can benefit from smart analytics, processing power and human intuition to utilize dynamic pricing.

Real Life Application: Airbnb:

Airbnb

Compared to other property websites, Airbnb faces a complicated pricing challenge.

Instead of the company, hosts have the power to set the price of their properties.

To help hosts make better decisions, Airbnb provides price comparison tools and data.

Airbnb realizes that most hosts will immediately search for similar properties upon signup.

This is to help them find a benchmark price for their properties.

Problems arise when hosts cannot find similar properties.

Therefore, Airbnb uses machine learning to automate the property comparison process.

This technology also incorporates supply and demand dynamics.

As a result, Airbnb can provide efficient pricing tools for hosts.

It also enables the hosts to receive ongoing guidance based on the market conditions.

3. Fraud Detection

As computer technologies and e-commerce are advancing, users are highly vulnerable to fraud.

Online criminals are going to great lengths to commit illegal acts.

These online hackers are continuously finding new ways to target victims.

In fact, anyone who utilizes online payment sources is exposed to this risk.

Traditionally, organizations use rules or logic statements to counter this.

Additionally, human reviews are done to back up this approach.

Admittedly, rule-based systems do a good job of uncovering known patterns.

However, they do not excel at uncovering unknown patterns.

When dealing with fraudsters that are coming up with new techniques every day, organizations need to step up.

This is where machine learning plays a role.

As it can review large sets of data, this provides organizations with the speed, scale and efficiency to be one step ahead of scammers.

Real Life Application: Paypal:

Paypal

In 2017, PayPal processed over $141 billion worth of transactions.

It is easy to understand why PayPal has an interest in fraud detection.

Many online scams have been targeting its platform users.

Hence, PayPal uses machine learning to be at the forefront of protecting its users.

To protect their users, PayPal relies on intensive data analysis in real time.

It uses the technology to study the buying behavior of individual customers.

The system immediately raises a red flag if an abnormal pattern is detected.

PayPal is also using machine learning to fight money laundering.

This helps PayPal to compare billions of transactions.

Ultimately, it helps PayPal identify fraudulent transactions.

The results are remarkable.

According to LexisNexis, PayPal keeps their fraud rate low at a 0.32% of revenue.

This is a great achievement compared to the 1.32% industry average.

Machine Learning Cons

Cons

  1. Humanity and human judgment erodes
  2. Exposed to human bias
  3. Widen the digital divide

The potential for good with machine learning is huge.

Similarly, the potential for misuse and abuse might be greater.

Let’s learn more about the disadvantages of using machine learning.

1. Humanity and Human Judgment Erodes

There have been significant advancements in algorithms.

Now, corporations and governments can analyze huge datasets easily.

To understand how machine learning can be disadvantageous, we need to understand the aim of machine learning.

Essentially, it aims to optimize efficiency and profitability.

It capitalizes on convenience and profit.

With this in mind, the technology does not consider possible societal impacts.

This can effectively discriminate certain populations.

In fact, humans are simply considered as an input to the system.

As a result, this creates inherent flaws in a logic-driven society.

The reality is machine learning is not as inclusive as we think.

It does not reflect the capacity of a person’s needs, wants, hopes and desires.

Moreover, there is a bigger issue with machine learning.

There is no data transparency.

Who is making money from the data collected?

How can someone know that their data is being collected?

For what purpose is the data used?

This exposes users to manipulation.

Profit-seeking intentions can be repackaged for the greater societal good.

An example of this is differentiated pricing.

Although it appears like it benefits the consumer, it helps the company that is selling things.

Algorithms can have a terrifying power over consumers decisions.

It can shape our decisions without us realizing it.

In fact, you can even say that it can read our minds.

Hence, this places those who have control over algorithms in an unfair position of power.

This can create more inequality in our world.

In this age, it is important that we assess the negative individual impact of machine learning.

This is seemingly the largest collateral damage in the name of progress.

2. Exposed to Human Bias

In a study conducted by Princeton University, researchers found that algorithms can be a reflection of their creators.

This can prove to be problematic.

As machine learning analyzes previous data, it can learn cultural biases.

In fact, it can learn to have a preference and have discriminatory views.

Two parties establish machine learning.

Firstly, the algorithm creators code the input.

They strive for objectivity and neutrality.

However, they tend to project their perspectives and values in their creation.

Secondly, these are the datasets on which these algorithms are applied to.

Although useful, datasets cannot reflect the fullness of people lives.

Hence, they are inherently flawed.

A study done by Massachusetts Institute of Technology revealed that algorithms would be primarily designed by white and Asian men.

Hence, they tend to select data to benefit consumers like themselves.

This is very harmful to those who are already in a position of disadvantage.

It can cause this group of people to experience a further halt in progress.

Meanwhile, it benefits people who are in positions of privilege.

Hence, an important challenge lies ahead of humankind.

We must find a balance between progressing the ability of machine learning and preventing unfair discrimination from happening.

Reality is, algorithm creators can come from inequality.

Hence, their algorithm will reflect their biased thinking.

This will inevitably reinforce inequalities.

Potentially damaging decisions and institutionalized bias can result from this.

The power that algorithm producers hold is massive.

Ultimately, there is a pressing need for algorithms to become open source and can be modified by user feedback.

3. Widen the Digital Divide

There are concerns that machine learning can further widen the digital gap.

This is a gap between digitally savvy people versus those who don’t have much access to the Internet.

Furthermore, social and political divisions can potentially occur due to algorithms.

Those with less access can be vulnerable to different risks.

Machine learning can potentially quarantine users into distinct ideological areas.

In fact, our capacity for empathy may suffer greatly.

In the years ahead of us, it will be interesting to see how major social media platforms will implement machine learning.

Ultimately, this will determine the structure of our information flow.

Depending on how it will be done, society can either be impacted positively or negatively.

On one hand, wealth disparity and discrimination can occur on a larger scale.

On the other hand, this can transform how economies function altogether.

The answer to whether machine learning will cause a further divide relies on who controls the technology.

One thing to keep in mind is algorithm will always be rooted in the value systems of their creator.

Without control, major corporations will dominate opportunities.

This leaves little room for small and medium businesses to flourish.

Today, we get a glimpse at how much power algorithms holds.

Without proper accountability for the technology, we may be heading towards a future which has extreme separation in worldviews.

The Future for Machine Learning

The Future

In this article, you have learnt the capabilities of machine learning.

Inherently, it helps humankind to make better decisions.

Also, it enables intensive data analysis that can’t be done with human capacity.

Its ability to process a huge amount of data makes it applicable to different industries.

Many industries urgently need machine learning to obtain actionable insights from their data.

Hence, the growth of machine learning is undeniable.

Whether you realize it or not, it is present in every area you can imagine.

From scrolling through your Facebook feed to buying a house, machine learning manifests itself in different ways.

The main positive impact of machine learning is it helps us understand how to make better decisions.

The algorithms which are the core of machine learning make this possible.

Ultimately, extensive trial and error  provides high-value predictions to organizations.

It provides speed, scale and efficiency in record time.

With the potential that comes with machine learning, it also bears negative impacts.

It dehumanizes the decision process.

Furthermore, it also portrays the biases of its creators.

Also, it widens the digital gap.

To counter these issues, we must ask ourselves an important question.

How can we fully understand the implications of algorithms?

By instilling education about how algorithms function, more people can learn more about its benefits and consequences.

There is also an increasing need for those who create algorithms in machine learning to be accountable to society.

Hence, proper governance and accountability structures must be in place.

It will take some time for us to develop the wisdom and ethics to understand and direct machine learning.

The smartest minds in the industry will have to sit together to solve the problem.

Until then, it is vital for us to comprehend how and who is accountable for machine learning.

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